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Design parameters and environmental impact of printed wiring board manufacture
Journal Pre-proof Design Parameters and Environmental Impact of Printed Wiring Board Manufacture
Maria Lourdes Alcaraz Ochoa, Haoyang He, Julie M. Schoenung, Erkko Helminen, Tom Okrasinki, Bill Schaeffer, Brian Smith, John Davignon, Larry Marcanti, Elsa A. Olivetti PII:
S0959-6526(19)32667-8
DOI:
https://doi.org/10.1016/j.jclepro.2019.117807
Article Number:
117807
Reference:
JCLP 117807
To appear in:
Journal of Cleaner Production
Received Date:
17 August 2018
Accepted Date:
25 July 2019
Please cite this article as: Maria Lourdes Alcaraz Ochoa, Haoyang He, Julie M. Schoenung, Erkko Helminen, Tom Okrasinki, Bill Schaeffer, Brian Smith, John Davignon, Larry Marcanti, Elsa A. Olivetti, Design Parameters and Environmental Impact of Printed Wiring Board Manufacture, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.117807
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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Design Parameters and Environmental Impact of Printed Wiring Board Manufacture
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1Department
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2Department
Maria Lourdes Alcaraz Ochoa1, Haoyang He2, Julie M. Schoenung2, Erkko Helminen3, Tom Okrasinki4, Bill Schaeffer4, Brian Smith5, John Davignon5, Larry Marcanti,5 Elsa A. Olivetti*1 of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of Materials Science & Engineering, University of California, Irvine, Irvine, CA
92697 3TTM
Technologies, Inc, Tai Po, New Territories Hong Kong SAR
4Nokia, 5High
Murray Hill, NJ 07974 United States
Density User Group Allen, TX 75013 United States
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Contact author: Elsa Olivetti* Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave, 8 – 403 Cambridge, MA 02139 [email protected] p: 617-253-0877
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word count: 5675
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ABSTRACT
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The environmental life cycle impact of electronics continues to be of interest within the life cycle arena. Previous work has shown the majority of burden can be attributed to the use phase as well as the manufacturing impact of components. This study leverages primary data from an industrial facility to provide an assessment of the cradle-to-gate global warming potential for printed wiring board (PWBs) components used in electronics equipment. There has not been as much evolution in the technology for PWBs as compared to other components such as integrated circuits. A newer technology, high-density interconnect (HDI) PWBs, is evaluated in addition to conventional boards based on various representative designs for consumer products. The results show that the board impact for handheld devices, notebooks and desktops range from to around 0.6 to 10 kgCO2e/board. The cradle-to-gate global warming potential is dominated by the manufacturing energy to fabricate the board as well as the board laminate materials (80% of the total impact). The study demonstrates that environmental impact varies by design parameters other than layer count and board area. The research also assesses the water use and chemical hazard associated with PWB manufacture.
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Key words: life cycle environmental impact, printed wiring boards, design parameters
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INTRODUCTION
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Information and communications technologies (ICT) are significant contributors to global greenhouse gas (GHG) emissions. Research has indicated that that the burden of ICT could climb from approximately 10% of global electricity use in 2010 to upwards of 20% by 2030 (Andrae and Edler, 2015). Within this significant energy footprint, the manufacturing and use phases typically dominate (more than phases such as distribution, packaging and assembly), with integrated circuits (ICs), displays (such as liquid crystal displays, LCDs) and printed wiring boards (PWB) manufacturing making up a significant portion of emissions as shown by several studies leveraging both streamlined, screening modeling approaches (Joyce et al., 2010), more detailed bottom up approaches focused on one technology (Emmenegger et al., 2006) or broader analyses (Arushanyan et al., 2014).
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As devices become more energy efficient and consumer dependence on mobile electronics increases, the use phase becomes proportionally less important from a per-device perspective, and embodied emissions make up a greater percentage of the impact (Park et al., 2006). This motivates continued investigation of cradle-to-gate impact including more detailed study of individual components. Despite the difficulties in environmental evaluation of complex, evolving products, understanding the impact of the manufacturing phase of electronic components continues to be a worthy endeavor towards identifying opportunities to mitigate this impact (Vasan et al., 2014).
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Of the studies available on environmental evaluation of electronic components, the majority have focused on the more rapidly changing components such as ICs (Boyd et al., 2009) and LCDs (Noon et al., 2011). Traditionally, PWB technology and PWB manufacturing processes have been more constant relative to electronic components like LCDs and ICs, and thus PWBs have not been the focus of environmental assessment to date beyond a few select studies. Lam et al. applied two environmental screening methods developed by U.S. EPA to explore the geographic and chemical environmental impacts of the PWB manufacturing industry in U.S. (Lam et al., 2011). Liu et al. designed a PWB using paper-based electronics technology and quantified its environmental impact with life cycle assessment (LCA) (Liu et al., 2014). Marques et al. summarized the structure and components of PWBs and the environmental problems associated with the production of PWBs with the suggestion that products should be designed to reduce their environmental impact (Marques et al., 2013). However, in order to reduce the impact of PWBs, the field needs improved knowledge on how design parameters are linked with environmental impact.
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Starting in the mid-1990s, the use of micro-via technology allowed high-density interconnect (HDI) constructions, enabling a significant transition in PWB technology and greatly reducing size (Brown 2016). HDI builds enable board thinning, decrease in layer count, and miniaturization of the device. Increased layer count is due to component pitch reduction and increased input/output (I/O) count, which in turn leads to thinner dielectric and conductor layers. The effects of thinner dielectrics and higher packing density are discussed by Helminen, et al. (Helminen et al., 2014). Miniaturization has reduced feature size (by more than a factor of two)
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while allowing more components and layers to be incorporated without increasing size, weight or volume of the assembly (Clyde, 2001). Introduction of HDI construction within PWB manufacturing provides an opportunity to assess the environmental assessment of PWBs as a function of design.
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This study presents the cradle-to-gate global warming potential (GWP), water usage and chemical hazard associated with PWBs that use HDI technology (as well as an assessment of the impact of conventional multi-layer PWBs) leveraging primary data from an industrial facility. In particular, the manuscript focuses on how board manufacturing impact changes as a function of key design parameters. The analysis below first shows how GWP varies with changes in area for board designs that are typical for consumer-facing products to provide a comparison with commercially available GWP data on PWBs focusing on manufacturing impact. Because current data do not provide insight into how impact varies with board design, the study demonstrates how GWP differs as a function of several typical design parameters. This impact is also broken down into contribution of laminate production, GWP of electricity consumption in board manufacture, and process chemical GWP. Quantifying only GWP provides a limited perspective on the impact of the manufacture and materials use of PWB production. Therefore, the analysis also provides a preliminary assessment of water use within the PWB fabrication facility, and the hazard associated with chemicals used within the PWB fabrication facility, both for manufacture of HDI-containing board construction. To connect these results to the GWP results, the changes in design examined above were related to changes in water and chemical use. Current available data on PWB manufacture only provide granularity on how that impact varies with board area and surface finish. Therefore, this work fills a key gap in the environmental literature by informing how impact changes as a function of design based on primary industry data, particularly as HDI technology has become prevalent in mobile devices.
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METHOD
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The goal of this work is to understand how the cradle-to-gate environmental impact of PWB fabrication, including GHG emissions, water consumption, and chemical hazard, varies depending on board design. Impact was determined, including uncertainty, using primary data from production facilities; this represents the first study to date to use such extensive primary data from PWB and laminate production facilities including conventional (CM) and HDI production. By providing a perspective on chemical hazard and water consumption along with the cradle-to-gate life cycle inventory data assessed using global warming potential, this study provides a set of environmental metrics driven and guided by the primary data available for this study. This approach could provide a basis for a more integrated way to link life cycle assessment with chemical hazard information.
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The boundaries of this study encompass the manufacturing phase of the PWB life cycle as well as the upstream impact of process chemicals and board materials. Only up to the manufacture of the finished boards are included within the study; the components that are added to a PWB to provide functionality to a device such as capacitors, resistors, and integrated circuits are not included in the analysis. Three elements are of particular focus; the data collection procedures and emissions calculations for each element are described in more detail below. (1) Materials and manufacturing of the laminate and process chemicals, which forms the base of the PWB, were examined through a combination of company data, existing literature and using a life cycle
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assessment approach. (2) The energy and water use of fabrication facilities was measured on-site and related to region-specific energy grid emissions. The energy data were integrated into the cradle-to-gate life cycle assessment and the water data were presented as separate inventory data (data on water throughout the rest of the life cycle were not available). Finally, (3) the hazard associated with process chemicals was assessed for the various types of process chemicals used on a chemical hazard assessment approach (details are provided below). Elements one and two were combined within an Excel based tool using Crystal Ball simulation add-on for the uncertainty analysis and element 3 was done in Excel outside of the simulation tool.
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As mentioned above, this manuscript investigates how impact changes with design parameters including the number of ‘buildup’ layers, ‘print and etch’ layers and buried vias. ‘Buildup’ describes the layers, which includes microblind, buried, and through vias that are made by processes other than mechanical drilling. These processes are repeated and the layers are built up, hence the name ‘buildup’ layer. ‘Print and etch’ layers are foil covered thin laminates that are imaged and then etched. They are then aligned in the stack and laminated. Buried vias are plated through holes that are internal to the printed circuit. They are made by drilling the holes then laminating into the stack. The motivation for the choice of these design parameters was to include those parameters that are accessible to a board design engineer while also connected directly to the process flow of the board (and therefore potentially linked to environmental impact). The design parameters and ranges used in this study were developed in conjunction with industry experts on board design technology and were aligned with specific product needs.
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PWB Fabrication Energy and Water Use
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Data were collected from two high volume, high efficiency manufacturing facilities in China that represent typical consumer product manufacturing facilities. Both plants run 24 hours a day, 7 days a week, powered solely by electricity. Facility A primarily manufactures CM PWB, but also produces smaller volumes of high layer count boards and some HDI designs. It produces approximately 925,000 m2 of PWB per year, including both CM and HDI designs. Facility B is designed specifically for the production of HDI PWBs, with an annual capacity of over 525,000 m2.
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Electricity and water usage was measured via meters placed on a component level, with each component encompassing a defined set of manufacturing process steps (as shown, schematically, in Figure 1), where several of the steps are repeated within each of the five board components (the inner layer, the core layer, the buildup layer, the outer layer and the surface finish layer). The HDI designs provide additional functionality that can enable reduced layer count and should not be directly compared on a per area basis, for further information on these steps please see (Clyde, 2001). Data were collected over a two month (56-day) period including low production periods. This time period was selected to present a typical production period for the facility. The daily PWB production during this period was recorded.
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Figure 1. Schematic of process flow steps for both a) CM and b) HDI PWB fabrication. These process flows were derived from Facility A and B, whose data were used in this study. The inputs of electricity, water, and process chemicals were measured by each component (colored sections) PTH=plated through hole, Mech. Drilling = Mechanical Drilling
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The raw data were examined for consistency; outliers were identified using the Generalized Extreme Studentized Deviate Test (after testing for normality within the data), which detects outlying points in a data set that follows a normalized distribution (Saltelli et al., 2000). Outliers were examined for accuracy, then deleted if determined to truly represent outlying data. Outliers accounted for less than 3% of the data, and over 90% of these outliers were zeros from data points that did not save correctly by the automated data loggers. The remaining data points were used to calculate the average energy use and water consumption per square meter of PWB for component (or set of processes).
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The fuel mix for each region where manufacturing occurred was taken from a 2015 International Energy Agency (IEA) report and then extrapolated up to a global average used throughout the results shown here (Sieminski, 2015). The greenhouse gas emission factor related to the use of each fuel (CO2 per kW of electricity generated) was also derived from IEA.
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Process Chemical Use
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For this study, only the process chemicals used within Facilities A and B that represented 80% of the total chemical usage by consumption mass were analyzed those. The authors note that this is a limitation of the current study as 20% by mass excluded from the process chemicals may account for a potentially significant source of impact. While detailed data on the remaining 20% were not available, preliminary indications suggest that these chemicals do not correspond to high hazard or energy consumption in production. The process chemicals were distributed into their raw chemical constituents using materials data sheets from the company participants. The rate of chemical use was determined by the consumption mass per component averaged over the square meter of produced material (laminate/PWB). Emissions factors to find the GHG emissions per kilogram of each raw chemical were chosen based on a methodology that underspecifies impact in order to capture the uncertainty associated with the chemical identification done through the Material Data Safety Sheet (MSDS) (Olivetti et al., 2013). Through this method, the total impact per square meter for the set of chemicals used for each component were then determined. Laminate Manufacturing
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Laminate is supplied to Facilities A and B from auxiliary manufacturers, representing potentially high embodied GHG emissions. Energy use data were obtained from two manufacturers; these data capture the energy use to produce the laminate sheets themselves. Primary data for energy used in making laminate components, such as glass, copper foil and epoxy resin, was not available. Therefore, the GHG impact was estimated based on emissions factors derived from company data and allocated to the laminate based on average component weight. Emissions for manufacturer energy use were calculated following the same method as for PWB production above, and then added to the component emissions for a total per square meter of laminate.
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Uncertainty Analysis
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The goal of the uncertainty analysis was to understand the impact of uncertainty on the results of the GHG emissions to determine where further data collection might have significant leverage on the uncertainty in the result. For both the HDI and CM PWB fabrication data uncertainty was estimated empirically from data fits based on measurements across 56 days within the facility. The following categories of data were calculated in this manner, the output, electricity consumption, and water consumption for each of the steps within the process flow. These data points were fit to the most likely distributions based on an Anderson-Darling test (Saltelli et al., 2000).
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The remaining distributions were made with approximations of the uncertainty based on an estimate of the data quality. For laminate production uncertainty was added on the power for the pre impregnated composite material and the laminate (25% coefficient of variation [COV]; normalized mean). For chemicals use for both CM and HDI uncertainty was added to the amount of chemical required per area (30% COV) and on the impact factor which converts to kg-CO2e based on the assumed embodied energy of the chemical. The latter factor was determined by looking at the range of data within a particular chemicals category (alcohols for example, or organic solvents). Finally, uncertainty was added to the grid mix emissions factor based on the projected fuel mix for the region of interest. Each of these uncertainties was put in a cell within the calculator such that users can modify them if they’d like to understand the impact of increased uncertainty in one of the areas described above. All of this uncertainty was then aggregated to provide an overall uncertainty in the calculated results.
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Chemical Hazard Assessment
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Chemical hazard assessment (CHA) is an analytical approach to systematically evaluate a full range of human health, environmental and physical hazard traits associated with chemicals of interest. In recent years, since various government organizations are taking steps to restrict or even remove chemicals from commercial production, the CHA approach is designed to facilitate the choice of safer chemicals and to minimize the potential for unintended consequences (Lavoie et al., 2010).
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To conduct CHA the GreenScreen for Safer ChemicalsTM (GS), a decision framework developed by Clean Production Action (Action, 2009), was applied. The primary data source was GHSJapan (GHS-Japan), which is derived strictly following the Globally Harmonized System of Classification and Labeling (Silk, 2003). Within the context of CHA, hazard is defined as the potential to cause damage, harm or an adverse effect to humans or the environment. The scope of
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this work covers the chemicals used in the fabrication facilities without consideration for the fate, transport or exposure to these hazardous chemicals.
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By conducting GS-based assessments, the PWB manufacturing process was screened for hazardous chemicals, similar to previous studies on thin film and organic photovoltaics (Eisenberg et al., 2013; He et al., 2018). GS is a benchmark-based decision framework used to screen chemicals by dividing them into 5 benchmark scores (BMs): BM 1- Chemical of High Concern; BM 2- Use but Search for Safer Substitutes; BM 3- Use but Still Opportunity for Improvement; BM 4- Safer Chemical; and BM U- Unspecified due to Insufficient Data. The benchmarks were assigned by evaluating 20 hazard traits (shown in Table 1), which are grouped for convenience into five categories: Group I Human Health, Group IIA Human Health, Group IIB Human Health, Environmental Toxicity & Fate, and Physical Hazards. Fate was not included in this study as the information for these traits is uncertain. Furthermore, the GS assessments have not been reviewed or validated by a GS-certified practitioner or toxicologist. Therefore, the results presented here are referred to as “GS-based.”
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Table 1. Hazard end points included in benchmarking grouping of 21 hazard traits
Group Human Health Group I
Hazard traits Carcinogenicity (C), Mutagenicity & Genotoxicity (M), Reproductive Toxicity (R), Developmental Toxicity (D), Endocrine Activity (E) Human Health Group IIA Acute Mammalian Toxicity (AT), Systematic Toxicity & Organ Effects (ST-single), Neurotoxicity (N-single), Skin Irritation (IrS), Eye Irritation (IrE); Human Health Group IIB Systematic Toxicity & Organ Effects-Repeated Exposure sub-endpoint (ST-repeated), Neurotoxicity-Repeated Exposure sub-endpoint (Nrepeated), Skin Sensitization (SnS), Respiratory Sensitization (SnR), Environmental Toxicity & Acute Aquatic Toxicity (AA), Chronic Aquatic Toxicity (CA), Persistence Fate (P), Bioaccumulation (B) Physical Hazards Reactivity (Rx) and Flammability (F)
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RESULTS AND DISCUSSION
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The results begin with an assessment of the cradle-to-gate GWP impact for several specific PWB designs as a function of board area (initial results are also provided in (Helminen et al., 2017)) including a brief comparison to published data as well as an assessment of uncertainty. These baseline results are then expanded to include variation in GWP impact as a function of board design parameters. Then, the impacts of water and process chemicals use are provided.
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Figure 2 shows the GWP impact of different example PWBs for typical consumer-facing products. The impact of a CM board for a desktop and notebook is plotted as well as four different HDI boards, two for a notebook and two for a handheld product. These designs vary in the number of layers of each type as indicated in Table 2 (the notebook PWB impact, based on the actual board size, ranges from 4.3-8 kgCO2e, the handheld’s impact is around 0.06 kgCO2e, and the desktop impact is around 6 kgCO2e). CM and HDI boards should not be compared directly, as they provide different functionality as will be described further below. GWP increases with board area for both CM and HDI, a result that aligns with intuition. The slopes vary among the products, suggesting that the scaling is different across the set of design changes explored in these example boards. This result provides evidence that existing life cycle
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inventories for PWBs (from sources such as ecoinvent and PE international (Frischknecht et al., 2005; Version, 2016)), which are specified just by layer count and surface finish (rather than by build parameters), are likely to either under or overestimate impact (depending on the details of the boards being assessed). For a given board area the CM design has a lower impact, however, the intent is not to compare CM and HDI technologies on an area basis, as HDI provides additional functionality not captured in metrics such as square meters of board. The electroless nickel immersion gold (ENiG_HDI) surface finish has a higher GWP impact than equivalently built boards with an organic surface finish (OSP_HDI). Finally, as board complexity increases, GWP impact increases (Notebook_181_HDI to Notebook_363).
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A brief comparison with the per meter squared results from commercially available life cycle inventory databases indicates that the impact of this facility may be lower than those databases. While a direct quantitative comparison is ill-advised because the assumptions within each inventory are different, using an Asian-based electricity grid to provide energy, the GWP calculated from commercially available life cycle inventory databases ranged from around 1.5 times to 2.6 times higher than the values shown in Figure 2 for similar board designs (using CM technology) (Frischknecht et al., 2005; Gabi). For example, on a per area basis for CM PWBs, the impact for a 6 layer board is around 20% less, but this is only a preliminary benchmarking assessment.
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Figure 2: GWP Impact of PWBs as a function of board area for several representative designs. See Table 2 for label explanations. Please note, curves are not meant to be directly compared as they have different functionality. Inset shows board area up to 0.1 m2, more relevant for the boards mentioned in Table 2.
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Table 2: Design parameters used for reference designs Dimensions (mm) Construction type
Notebook 152 x 203
Desktop 203 x 305
CM
CM
Notebook_181 Notebook_363 Handheld_OSP Handheld_ENiG 152 x 203 152 x 203 127 x 25 127 x 25 HDI
HDI
HDI
HDI
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No. of P&EL1 4 2 4 3 3 3 pairs No. of BVL2 0 0 0 0 0 0 pairs No. of BUL3 NA NA 1 3 1 1 pairs No. of Prepreg 10 6 10 12 8 8 Sheets 1P&E: print and etch layer pairs. 2BVL: Internal buried via layer pairs. 3BUL: Buildup layer pairs.
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The results depicted in Figure 2 indicate that the total GWP emissions increase as the number of layers increase for both HDI and CM designs, but the amount of increase depends on the type of layer being added. This is a significant result as current life cycle inventory databases do not directly capture these sorts of design changes. As explained throughout, direct comparison between CM and HDI is difficult as the boards are designed for different functionality. The authors provide a brief discussion here to indicate how one might capture this additional capability of HDI in the form of improved circuit density, but readers are referred to the referenced source for further information. Although the technology continues to develop, general guidance by the industry states that the connections per square centimeter of board can be increased in density by between a factor of two and four for HDI. This implies that fewer layers can be used to provide a board with the same functionality. For example, one build up layer in HDI for a four layer board can provide the same number of connections as a 10 layer CM design. This can also lead to reduced area, upwards of 40% in some cases (Holden and Charbonneau, 2000). However, although the HDI process offers the opportunity to use fewer layers this does not necessarily mean that the GHG emissions would be less.
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For the uncertainty analysis, the COV for the representative designs (for the parameters shown in Figure 2) ranged from 14% - 20%. The statistical simulations show that the PWB fabrication accounted for greater than 50% of the impact in 70% of the trials and 60% of the impact in only 10% of trials. The contribution to variation was determined for each of the elements of the model (based on a Sobol method calculation (Sobol', 1990)). The emissions factor associated with electricity was the highest contributor to uncertainty for the board GWP impact. This is because of the ubiquity of electricity throughout the calculations in the laminate production and PWB fabrication specifically. The next most significant contributor to the uncertainty was the quantity of electricity consumed in the laminate and the emission factors associated with the process chemicals. This is consistent with expectation as these data were the most uncertain (obtained from upstream suppliers rather than direct facility measurement). Therefore, further data collection efforts could focus on laminate production as well as process chemical impact.
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The impact of design changes on overall GWP impact is shown by varying a few key board design parameters [Print & Etch Layer (P&EL) pairs, Buried Via Layer (BVL) pairs, and Buildup Layer (BUL) pairs]. The change in resulting GWP impact is shown Figure 3. These results show the cradle-to-gate GWP impact for HDI construction including 14 layer pre-preg with 1, or 3 BUL pairs in units of kgCO2e/m2 board. To depict the design change difference either the P&EL pairs or BVL pairs were changed in increments of 2, 4 and 6 (when the P&EL varies BVL is fixed at 2 and vice versa). An incremental increase in the number of internal BVL pairs results in an emissions increase of approximately 80 kgCO2e, while an analogous increase in P&EL pairs results in only half that increase (a change in BUL pair design results in a 30 kgCO2e change for fixed P&EL as well as BVL). While the two sets of lines begin at similar
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points for each BUL, they diverge as the other varying design parameter increases. This further underscores that GWP impact varies with more than just varying layer count and overall board area; a shift in P&EL provided the least shift in impact per parameter change while BVL and BUL provided similar larger relative impact change.
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Figure 3: Cradle-to-gate GWP impact based on changes in design parameters including P&EL, BVL and BUL pairs (for a 14 layer pre-preg board). BUL 1 shown in grey and BUL 3 show in black.
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Thus far, the results have examined the overall cradle-to-gate impact of PWB manufacture. It is also instructive to see how different steps in the process vary differently with the same design changes explored above. The individual contributors included in the cradle-to-gate the emissions are: 1) the laminate materials and production, 2) the PWB fabrication energy consumption, and 3) the embodied GWP emissions of process chemicals. An analysis of their contribution to the total emissions, shown in Figure 4, indicates that their % contribution to emissions changes as a function of the design parameters explored, but more significantly between the impact of HDI and CM. Figure 4a explores these changes in percent contribution 1 BVL pair as a function of P&EL pairs versus Figure 4b which shows the percent contribution for a CM board. In each figure, the percent contribution to GWP changes as a function of P&EL pairs. For HDI, as the number of P&EL pairs increases, the laminate process contribution increases and the process chemicals and PWB fabrication percentage contribution decreases. In addition, the laminate process contribution is higher for fewer BVL pairs. For CM boards as the number of P&EL pairs increases, the laminate process contribution increases and the process chemicals percent contribution decreases. The scaling is different as the design parameters change because the impact scales differently with each of the design parameters chosen.
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Figure 4: (a) Percent contribution to the carbon dioxide emissions in PWB fabrication of an HDI board as a function of P&EL pairs for each major process step with internal BVL pairs of 1. (b) Percent contribution to the carbon dioxide emissions in PWB fabrication of a CM board as a function of P&EL pairs for each major process step with internal BVL pairs of 0.
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Figure 5 shows how the components of the HDI board itself contribute to fabrication energy consumption (green), embodied emissions associated with chemical use (red) and water consumption (blue). These pie charts are shown for two P&EL pair examples (2 and 6) as well as two BVL pair examples (1 and 2). A breakdown of the contributors to the emissions by each component shows that the largest contributor to all three metrics is the inner layer. Furthermore, for all three metrics, the contribution of the inner layer to the total increases as the number of P&EL pairs and BVL pairs increase. This is shown explicitly for all three metrics: fabrication energy consumption, process chemicals and water use. The inner layer contribution is most significant for the higher number of internal BVL pairs. Linking this result to the one described in Figure 3, the number of internal BVL pairs provides a significant lever in design change related to board impact.
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Figure 5: The percent contribution of the inner layer, outer layer, core layer and surface finish to the fabrication energy consumption, water consumption and embodied process chemicals emissions as the number of P&EL pairs and BVL pairs increase.
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The results presented thus far have focused only on assessing the embodied GWP emissions associated with chemicals use (as well as a brief mention of water consumption); however, using GWP-indicators to evaluate the environmental impact of process chemicals provides only limited information on the environmental and human health impacts of these chemicals. To provide another perspective on chemicals use, a chemical hazard assessment based on GreenScreen is included and relate this analysis to the design changes explored above. The GS-based benchmark results for HDI technologies are shown in Figure 6. More than 80% of the chemicals received a BM 1 or BM 2, which means that a large percentage of these chemicals exhibit various human health, environmental and/or physical hazard traits. Of the studied process chemicals, one is a BM 1 chemical, which is triggered by its confirmed carcinogenicity; one of the BM 2 chemicals is triggered by suspected mutagenicity and reproductive toxicity; several BM 2 and BM 3 chemicals are triggered by their Group II and Group II* Human Health hazard traits (see Table 1); four BM 2 chemicals are also triggered by environmental hazard traits; and three chemicals are triggered by physical hazards.
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Figure 6: The percentage of GS-based benchmark (BM) scores for chemicals in HDI manufacturing and the number of chemicals with their respective benchmark scores for each process step and by component within the board. Each process step is used differently within the five components, as shown in the table.
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One can also examine the benchmark scores for the chemicals used by step. Particularly, of the 14 steps, the pattern plating consumes the most chemicals, whereas routing and etching use the least. There is one BM1 chemical, which is used in the plated-through hole (PTH) step, an important step used in the processing of all of the components, except the surface finish layer. This process chemical is, however, used differently in each of the steps within these components such that its contribution to the total chemical use increases by approximately 3% for a buildup layer increase and by 8% for a buried via layer increase, but decreases by 9% for print and etch layer increases. Therefore, as environmental considerations are made in board design, addressing the use of the hazardous chemicals in the PTH steps would be an area where significant gains could be achieved if alternatives could be identified. Next steps for this work will include a more detailed exploration of the impact of the quantity of process chemicals on the CHA.
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CONCLUSIONS
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To the best of the authors’ knowledge, current life cycle inventory databases do not include estimates for HDI technology and understanding how this impact scales with relevant design parameters can help product engineers begin to consider environmental impact along with technical performance. This study, based on primary industry data, has shown that these design parameters can influence the final board impact for both CM and HDI PWBs. The analysis has shown that it continues to be important to explore more than just the facility based electricity use for electronic components: water and chemicals use are also areas for environmental impact reduction. Furthermore, by examining both the GWP impact from a gate-to-gate life cycle assessment perspective as well as assessing chemical hazard, the authors have begun work towards a more integrated evaluation approach for chemical-intensive processes. In particular this multi-metric assessment has indicated which processing steps within the HDI process would have higher hazard relative to others.
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The authors also outline a few areas of future work (besides the CHA work mentioned above). The results of this study are based on just a few board manufacturing facilities, so a more
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comprehensive analysis should assess how the energy and water impact varies across facilities making similar technologies. The remainder of process chemicals should be included in further work and an analysis that lets designers weigh relative designs according to environmental impact would also be of interest. Two additional technologies that the industry has cited that may further reduce environmental impact towards cleaner production of PWBs include: solid paste vias that eliminate several steps including electroless copper, de-smear, panel plate and pattern plates in all but one lamination step. This would likely further reduce global warming potential. In addition, there has been a trend towards greater use of films and liquid dielectrics including thermoplastics that would further reduce the need for copper. PWB manufacture is one of the more distributed steps in electronics production so likely significant variation exists. As the energy efficiency in the use phase of electronic devices continues to go down, it will be important to further investigate the impact of component and materials manufacture.
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ACKNOWLEDGEMENTS
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The authors acknowledge the contributions of Happy Holden and Joseph Smetana. The authors acknowledge support from the Government of Portugal through the Portuguese Foundation for International Cooperation in Science, Technology, and Higher Education, and a portion of this work was undertaken in the MIT Portugal Program. The authors also acknowledge partial support provided by the University of California Irvine PhD Bridge Program.
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Highlights We analyze global warming, water, and chemical use for printed wiring boards Variation of impact with design of high density interconnect boards Impact driven by energy to fabricate boards and board laminate materials
Maria Lourdes Alcaraz Ochoa, Haoyang He, Julie M. Schoenung, Erkko Helminen, Tom Okrasinki, Bill Schaeffer, Brian Smith, John Davignon, Larry Marcanti, Elsa A. Olivetti PII:
S0959-6526(19)32667-8
DOI:
https://doi.org/10.1016/j.jclepro.2019.117807
Article Number:
117807
Reference:
JCLP 117807
To appear in:
Journal of Cleaner Production
Received Date:
17 August 2018
Accepted Date:
25 July 2019
Please cite this article as: Maria Lourdes Alcaraz Ochoa, Haoyang He, Julie M. Schoenung, Erkko Helminen, Tom Okrasinki, Bill Schaeffer, Brian Smith, John Davignon, Larry Marcanti, Elsa A. Olivetti, Design Parameters and Environmental Impact of Printed Wiring Board Manufacture, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.117807
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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Design Parameters and Environmental Impact of Printed Wiring Board Manufacture
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1Department
8 9
2Department
Maria Lourdes Alcaraz Ochoa1, Haoyang He2, Julie M. Schoenung2, Erkko Helminen3, Tom Okrasinki4, Bill Schaeffer4, Brian Smith5, John Davignon5, Larry Marcanti,5 Elsa A. Olivetti*1 of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of Materials Science & Engineering, University of California, Irvine, Irvine, CA
92697 3TTM
Technologies, Inc, Tai Po, New Territories Hong Kong SAR
4Nokia, 5High
Murray Hill, NJ 07974 United States
Density User Group Allen, TX 75013 United States
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Contact author: Elsa Olivetti* Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave, 8 – 403 Cambridge, MA 02139 [email protected] p: 617-253-0877
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word count: 5675
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ABSTRACT
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The environmental life cycle impact of electronics continues to be of interest within the life cycle arena. Previous work has shown the majority of burden can be attributed to the use phase as well as the manufacturing impact of components. This study leverages primary data from an industrial facility to provide an assessment of the cradle-to-gate global warming potential for printed wiring board (PWBs) components used in electronics equipment. There has not been as much evolution in the technology for PWBs as compared to other components such as integrated circuits. A newer technology, high-density interconnect (HDI) PWBs, is evaluated in addition to conventional boards based on various representative designs for consumer products. The results show that the board impact for handheld devices, notebooks and desktops range from to around 0.6 to 10 kgCO2e/board. The cradle-to-gate global warming potential is dominated by the manufacturing energy to fabricate the board as well as the board laminate materials (80% of the total impact). The study demonstrates that environmental impact varies by design parameters other than layer count and board area. The research also assesses the water use and chemical hazard associated with PWB manufacture.
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Key words: life cycle environmental impact, printed wiring boards, design parameters
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INTRODUCTION
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Information and communications technologies (ICT) are significant contributors to global greenhouse gas (GHG) emissions. Research has indicated that that the burden of ICT could climb from approximately 10% of global electricity use in 2010 to upwards of 20% by 2030 (Andrae and Edler, 2015). Within this significant energy footprint, the manufacturing and use phases typically dominate (more than phases such as distribution, packaging and assembly), with integrated circuits (ICs), displays (such as liquid crystal displays, LCDs) and printed wiring boards (PWB) manufacturing making up a significant portion of emissions as shown by several studies leveraging both streamlined, screening modeling approaches (Joyce et al., 2010), more detailed bottom up approaches focused on one technology (Emmenegger et al., 2006) or broader analyses (Arushanyan et al., 2014).
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As devices become more energy efficient and consumer dependence on mobile electronics increases, the use phase becomes proportionally less important from a per-device perspective, and embodied emissions make up a greater percentage of the impact (Park et al., 2006). This motivates continued investigation of cradle-to-gate impact including more detailed study of individual components. Despite the difficulties in environmental evaluation of complex, evolving products, understanding the impact of the manufacturing phase of electronic components continues to be a worthy endeavor towards identifying opportunities to mitigate this impact (Vasan et al., 2014).
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Of the studies available on environmental evaluation of electronic components, the majority have focused on the more rapidly changing components such as ICs (Boyd et al., 2009) and LCDs (Noon et al., 2011). Traditionally, PWB technology and PWB manufacturing processes have been more constant relative to electronic components like LCDs and ICs, and thus PWBs have not been the focus of environmental assessment to date beyond a few select studies. Lam et al. applied two environmental screening methods developed by U.S. EPA to explore the geographic and chemical environmental impacts of the PWB manufacturing industry in U.S. (Lam et al., 2011). Liu et al. designed a PWB using paper-based electronics technology and quantified its environmental impact with life cycle assessment (LCA) (Liu et al., 2014). Marques et al. summarized the structure and components of PWBs and the environmental problems associated with the production of PWBs with the suggestion that products should be designed to reduce their environmental impact (Marques et al., 2013). However, in order to reduce the impact of PWBs, the field needs improved knowledge on how design parameters are linked with environmental impact.
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Starting in the mid-1990s, the use of micro-via technology allowed high-density interconnect (HDI) constructions, enabling a significant transition in PWB technology and greatly reducing size (Brown 2016). HDI builds enable board thinning, decrease in layer count, and miniaturization of the device. Increased layer count is due to component pitch reduction and increased input/output (I/O) count, which in turn leads to thinner dielectric and conductor layers. The effects of thinner dielectrics and higher packing density are discussed by Helminen, et al. (Helminen et al., 2014). Miniaturization has reduced feature size (by more than a factor of two)
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while allowing more components and layers to be incorporated without increasing size, weight or volume of the assembly (Clyde, 2001). Introduction of HDI construction within PWB manufacturing provides an opportunity to assess the environmental assessment of PWBs as a function of design.
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This study presents the cradle-to-gate global warming potential (GWP), water usage and chemical hazard associated with PWBs that use HDI technology (as well as an assessment of the impact of conventional multi-layer PWBs) leveraging primary data from an industrial facility. In particular, the manuscript focuses on how board manufacturing impact changes as a function of key design parameters. The analysis below first shows how GWP varies with changes in area for board designs that are typical for consumer-facing products to provide a comparison with commercially available GWP data on PWBs focusing on manufacturing impact. Because current data do not provide insight into how impact varies with board design, the study demonstrates how GWP differs as a function of several typical design parameters. This impact is also broken down into contribution of laminate production, GWP of electricity consumption in board manufacture, and process chemical GWP. Quantifying only GWP provides a limited perspective on the impact of the manufacture and materials use of PWB production. Therefore, the analysis also provides a preliminary assessment of water use within the PWB fabrication facility, and the hazard associated with chemicals used within the PWB fabrication facility, both for manufacture of HDI-containing board construction. To connect these results to the GWP results, the changes in design examined above were related to changes in water and chemical use. Current available data on PWB manufacture only provide granularity on how that impact varies with board area and surface finish. Therefore, this work fills a key gap in the environmental literature by informing how impact changes as a function of design based on primary industry data, particularly as HDI technology has become prevalent in mobile devices.
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METHOD
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The goal of this work is to understand how the cradle-to-gate environmental impact of PWB fabrication, including GHG emissions, water consumption, and chemical hazard, varies depending on board design. Impact was determined, including uncertainty, using primary data from production facilities; this represents the first study to date to use such extensive primary data from PWB and laminate production facilities including conventional (CM) and HDI production. By providing a perspective on chemical hazard and water consumption along with the cradle-to-gate life cycle inventory data assessed using global warming potential, this study provides a set of environmental metrics driven and guided by the primary data available for this study. This approach could provide a basis for a more integrated way to link life cycle assessment with chemical hazard information.
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The boundaries of this study encompass the manufacturing phase of the PWB life cycle as well as the upstream impact of process chemicals and board materials. Only up to the manufacture of the finished boards are included within the study; the components that are added to a PWB to provide functionality to a device such as capacitors, resistors, and integrated circuits are not included in the analysis. Three elements are of particular focus; the data collection procedures and emissions calculations for each element are described in more detail below. (1) Materials and manufacturing of the laminate and process chemicals, which forms the base of the PWB, were examined through a combination of company data, existing literature and using a life cycle
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assessment approach. (2) The energy and water use of fabrication facilities was measured on-site and related to region-specific energy grid emissions. The energy data were integrated into the cradle-to-gate life cycle assessment and the water data were presented as separate inventory data (data on water throughout the rest of the life cycle were not available). Finally, (3) the hazard associated with process chemicals was assessed for the various types of process chemicals used on a chemical hazard assessment approach (details are provided below). Elements one and two were combined within an Excel based tool using Crystal Ball simulation add-on for the uncertainty analysis and element 3 was done in Excel outside of the simulation tool.
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As mentioned above, this manuscript investigates how impact changes with design parameters including the number of ‘buildup’ layers, ‘print and etch’ layers and buried vias. ‘Buildup’ describes the layers, which includes microblind, buried, and through vias that are made by processes other than mechanical drilling. These processes are repeated and the layers are built up, hence the name ‘buildup’ layer. ‘Print and etch’ layers are foil covered thin laminates that are imaged and then etched. They are then aligned in the stack and laminated. Buried vias are plated through holes that are internal to the printed circuit. They are made by drilling the holes then laminating into the stack. The motivation for the choice of these design parameters was to include those parameters that are accessible to a board design engineer while also connected directly to the process flow of the board (and therefore potentially linked to environmental impact). The design parameters and ranges used in this study were developed in conjunction with industry experts on board design technology and were aligned with specific product needs.
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PWB Fabrication Energy and Water Use
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Data were collected from two high volume, high efficiency manufacturing facilities in China that represent typical consumer product manufacturing facilities. Both plants run 24 hours a day, 7 days a week, powered solely by electricity. Facility A primarily manufactures CM PWB, but also produces smaller volumes of high layer count boards and some HDI designs. It produces approximately 925,000 m2 of PWB per year, including both CM and HDI designs. Facility B is designed specifically for the production of HDI PWBs, with an annual capacity of over 525,000 m2.
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Electricity and water usage was measured via meters placed on a component level, with each component encompassing a defined set of manufacturing process steps (as shown, schematically, in Figure 1), where several of the steps are repeated within each of the five board components (the inner layer, the core layer, the buildup layer, the outer layer and the surface finish layer). The HDI designs provide additional functionality that can enable reduced layer count and should not be directly compared on a per area basis, for further information on these steps please see (Clyde, 2001). Data were collected over a two month (56-day) period including low production periods. This time period was selected to present a typical production period for the facility. The daily PWB production during this period was recorded.
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Figure 1. Schematic of process flow steps for both a) CM and b) HDI PWB fabrication. These process flows were derived from Facility A and B, whose data were used in this study. The inputs of electricity, water, and process chemicals were measured by each component (colored sections) PTH=plated through hole, Mech. Drilling = Mechanical Drilling
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The raw data were examined for consistency; outliers were identified using the Generalized Extreme Studentized Deviate Test (after testing for normality within the data), which detects outlying points in a data set that follows a normalized distribution (Saltelli et al., 2000). Outliers were examined for accuracy, then deleted if determined to truly represent outlying data. Outliers accounted for less than 3% of the data, and over 90% of these outliers were zeros from data points that did not save correctly by the automated data loggers. The remaining data points were used to calculate the average energy use and water consumption per square meter of PWB for component (or set of processes).
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The fuel mix for each region where manufacturing occurred was taken from a 2015 International Energy Agency (IEA) report and then extrapolated up to a global average used throughout the results shown here (Sieminski, 2015). The greenhouse gas emission factor related to the use of each fuel (CO2 per kW of electricity generated) was also derived from IEA.
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Process Chemical Use
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For this study, only the process chemicals used within Facilities A and B that represented 80% of the total chemical usage by consumption mass were analyzed those. The authors note that this is a limitation of the current study as 20% by mass excluded from the process chemicals may account for a potentially significant source of impact. While detailed data on the remaining 20% were not available, preliminary indications suggest that these chemicals do not correspond to high hazard or energy consumption in production. The process chemicals were distributed into their raw chemical constituents using materials data sheets from the company participants. The rate of chemical use was determined by the consumption mass per component averaged over the square meter of produced material (laminate/PWB). Emissions factors to find the GHG emissions per kilogram of each raw chemical were chosen based on a methodology that underspecifies impact in order to capture the uncertainty associated with the chemical identification done through the Material Data Safety Sheet (MSDS) (Olivetti et al., 2013). Through this method, the total impact per square meter for the set of chemicals used for each component were then determined. Laminate Manufacturing
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Laminate is supplied to Facilities A and B from auxiliary manufacturers, representing potentially high embodied GHG emissions. Energy use data were obtained from two manufacturers; these data capture the energy use to produce the laminate sheets themselves. Primary data for energy used in making laminate components, such as glass, copper foil and epoxy resin, was not available. Therefore, the GHG impact was estimated based on emissions factors derived from company data and allocated to the laminate based on average component weight. Emissions for manufacturer energy use were calculated following the same method as for PWB production above, and then added to the component emissions for a total per square meter of laminate.
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Uncertainty Analysis
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The goal of the uncertainty analysis was to understand the impact of uncertainty on the results of the GHG emissions to determine where further data collection might have significant leverage on the uncertainty in the result. For both the HDI and CM PWB fabrication data uncertainty was estimated empirically from data fits based on measurements across 56 days within the facility. The following categories of data were calculated in this manner, the output, electricity consumption, and water consumption for each of the steps within the process flow. These data points were fit to the most likely distributions based on an Anderson-Darling test (Saltelli et al., 2000).
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The remaining distributions were made with approximations of the uncertainty based on an estimate of the data quality. For laminate production uncertainty was added on the power for the pre impregnated composite material and the laminate (25% coefficient of variation [COV]; normalized mean). For chemicals use for both CM and HDI uncertainty was added to the amount of chemical required per area (30% COV) and on the impact factor which converts to kg-CO2e based on the assumed embodied energy of the chemical. The latter factor was determined by looking at the range of data within a particular chemicals category (alcohols for example, or organic solvents). Finally, uncertainty was added to the grid mix emissions factor based on the projected fuel mix for the region of interest. Each of these uncertainties was put in a cell within the calculator such that users can modify them if they’d like to understand the impact of increased uncertainty in one of the areas described above. All of this uncertainty was then aggregated to provide an overall uncertainty in the calculated results.
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Chemical Hazard Assessment
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Chemical hazard assessment (CHA) is an analytical approach to systematically evaluate a full range of human health, environmental and physical hazard traits associated with chemicals of interest. In recent years, since various government organizations are taking steps to restrict or even remove chemicals from commercial production, the CHA approach is designed to facilitate the choice of safer chemicals and to minimize the potential for unintended consequences (Lavoie et al., 2010).
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To conduct CHA the GreenScreen for Safer ChemicalsTM (GS), a decision framework developed by Clean Production Action (Action, 2009), was applied. The primary data source was GHSJapan (GHS-Japan), which is derived strictly following the Globally Harmonized System of Classification and Labeling (Silk, 2003). Within the context of CHA, hazard is defined as the potential to cause damage, harm or an adverse effect to humans or the environment. The scope of
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this work covers the chemicals used in the fabrication facilities without consideration for the fate, transport or exposure to these hazardous chemicals.
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By conducting GS-based assessments, the PWB manufacturing process was screened for hazardous chemicals, similar to previous studies on thin film and organic photovoltaics (Eisenberg et al., 2013; He et al., 2018). GS is a benchmark-based decision framework used to screen chemicals by dividing them into 5 benchmark scores (BMs): BM 1- Chemical of High Concern; BM 2- Use but Search for Safer Substitutes; BM 3- Use but Still Opportunity for Improvement; BM 4- Safer Chemical; and BM U- Unspecified due to Insufficient Data. The benchmarks were assigned by evaluating 20 hazard traits (shown in Table 1), which are grouped for convenience into five categories: Group I Human Health, Group IIA Human Health, Group IIB Human Health, Environmental Toxicity & Fate, and Physical Hazards. Fate was not included in this study as the information for these traits is uncertain. Furthermore, the GS assessments have not been reviewed or validated by a GS-certified practitioner or toxicologist. Therefore, the results presented here are referred to as “GS-based.”
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Table 1. Hazard end points included in benchmarking grouping of 21 hazard traits
Group Human Health Group I
Hazard traits Carcinogenicity (C), Mutagenicity & Genotoxicity (M), Reproductive Toxicity (R), Developmental Toxicity (D), Endocrine Activity (E) Human Health Group IIA Acute Mammalian Toxicity (AT), Systematic Toxicity & Organ Effects (ST-single), Neurotoxicity (N-single), Skin Irritation (IrS), Eye Irritation (IrE); Human Health Group IIB Systematic Toxicity & Organ Effects-Repeated Exposure sub-endpoint (ST-repeated), Neurotoxicity-Repeated Exposure sub-endpoint (Nrepeated), Skin Sensitization (SnS), Respiratory Sensitization (SnR), Environmental Toxicity & Acute Aquatic Toxicity (AA), Chronic Aquatic Toxicity (CA), Persistence Fate (P), Bioaccumulation (B) Physical Hazards Reactivity (Rx) and Flammability (F)
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RESULTS AND DISCUSSION
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The results begin with an assessment of the cradle-to-gate GWP impact for several specific PWB designs as a function of board area (initial results are also provided in (Helminen et al., 2017)) including a brief comparison to published data as well as an assessment of uncertainty. These baseline results are then expanded to include variation in GWP impact as a function of board design parameters. Then, the impacts of water and process chemicals use are provided.
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Figure 2 shows the GWP impact of different example PWBs for typical consumer-facing products. The impact of a CM board for a desktop and notebook is plotted as well as four different HDI boards, two for a notebook and two for a handheld product. These designs vary in the number of layers of each type as indicated in Table 2 (the notebook PWB impact, based on the actual board size, ranges from 4.3-8 kgCO2e, the handheld’s impact is around 0.06 kgCO2e, and the desktop impact is around 6 kgCO2e). CM and HDI boards should not be compared directly, as they provide different functionality as will be described further below. GWP increases with board area for both CM and HDI, a result that aligns with intuition. The slopes vary among the products, suggesting that the scaling is different across the set of design changes explored in these example boards. This result provides evidence that existing life cycle
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inventories for PWBs (from sources such as ecoinvent and PE international (Frischknecht et al., 2005; Version, 2016)), which are specified just by layer count and surface finish (rather than by build parameters), are likely to either under or overestimate impact (depending on the details of the boards being assessed). For a given board area the CM design has a lower impact, however, the intent is not to compare CM and HDI technologies on an area basis, as HDI provides additional functionality not captured in metrics such as square meters of board. The electroless nickel immersion gold (ENiG_HDI) surface finish has a higher GWP impact than equivalently built boards with an organic surface finish (OSP_HDI). Finally, as board complexity increases, GWP impact increases (Notebook_181_HDI to Notebook_363).
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A brief comparison with the per meter squared results from commercially available life cycle inventory databases indicates that the impact of this facility may be lower than those databases. While a direct quantitative comparison is ill-advised because the assumptions within each inventory are different, using an Asian-based electricity grid to provide energy, the GWP calculated from commercially available life cycle inventory databases ranged from around 1.5 times to 2.6 times higher than the values shown in Figure 2 for similar board designs (using CM technology) (Frischknecht et al., 2005; Gabi). For example, on a per area basis for CM PWBs, the impact for a 6 layer board is around 20% less, but this is only a preliminary benchmarking assessment.
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Figure 2: GWP Impact of PWBs as a function of board area for several representative designs. See Table 2 for label explanations. Please note, curves are not meant to be directly compared as they have different functionality. Inset shows board area up to 0.1 m2, more relevant for the boards mentioned in Table 2.
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Table 2: Design parameters used for reference designs Dimensions (mm) Construction type
Notebook 152 x 203
Desktop 203 x 305
CM
CM
Notebook_181 Notebook_363 Handheld_OSP Handheld_ENiG 152 x 203 152 x 203 127 x 25 127 x 25 HDI
HDI
HDI
HDI
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No. of P&EL1 4 2 4 3 3 3 pairs No. of BVL2 0 0 0 0 0 0 pairs No. of BUL3 NA NA 1 3 1 1 pairs No. of Prepreg 10 6 10 12 8 8 Sheets 1P&E: print and etch layer pairs. 2BVL: Internal buried via layer pairs. 3BUL: Buildup layer pairs.
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The results depicted in Figure 2 indicate that the total GWP emissions increase as the number of layers increase for both HDI and CM designs, but the amount of increase depends on the type of layer being added. This is a significant result as current life cycle inventory databases do not directly capture these sorts of design changes. As explained throughout, direct comparison between CM and HDI is difficult as the boards are designed for different functionality. The authors provide a brief discussion here to indicate how one might capture this additional capability of HDI in the form of improved circuit density, but readers are referred to the referenced source for further information. Although the technology continues to develop, general guidance by the industry states that the connections per square centimeter of board can be increased in density by between a factor of two and four for HDI. This implies that fewer layers can be used to provide a board with the same functionality. For example, one build up layer in HDI for a four layer board can provide the same number of connections as a 10 layer CM design. This can also lead to reduced area, upwards of 40% in some cases (Holden and Charbonneau, 2000). However, although the HDI process offers the opportunity to use fewer layers this does not necessarily mean that the GHG emissions would be less.
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For the uncertainty analysis, the COV for the representative designs (for the parameters shown in Figure 2) ranged from 14% - 20%. The statistical simulations show that the PWB fabrication accounted for greater than 50% of the impact in 70% of the trials and 60% of the impact in only 10% of trials. The contribution to variation was determined for each of the elements of the model (based on a Sobol method calculation (Sobol', 1990)). The emissions factor associated with electricity was the highest contributor to uncertainty for the board GWP impact. This is because of the ubiquity of electricity throughout the calculations in the laminate production and PWB fabrication specifically. The next most significant contributor to the uncertainty was the quantity of electricity consumed in the laminate and the emission factors associated with the process chemicals. This is consistent with expectation as these data were the most uncertain (obtained from upstream suppliers rather than direct facility measurement). Therefore, further data collection efforts could focus on laminate production as well as process chemical impact.
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The impact of design changes on overall GWP impact is shown by varying a few key board design parameters [Print & Etch Layer (P&EL) pairs, Buried Via Layer (BVL) pairs, and Buildup Layer (BUL) pairs]. The change in resulting GWP impact is shown Figure 3. These results show the cradle-to-gate GWP impact for HDI construction including 14 layer pre-preg with 1, or 3 BUL pairs in units of kgCO2e/m2 board. To depict the design change difference either the P&EL pairs or BVL pairs were changed in increments of 2, 4 and 6 (when the P&EL varies BVL is fixed at 2 and vice versa). An incremental increase in the number of internal BVL pairs results in an emissions increase of approximately 80 kgCO2e, while an analogous increase in P&EL pairs results in only half that increase (a change in BUL pair design results in a 30 kgCO2e change for fixed P&EL as well as BVL). While the two sets of lines begin at similar
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points for each BUL, they diverge as the other varying design parameter increases. This further underscores that GWP impact varies with more than just varying layer count and overall board area; a shift in P&EL provided the least shift in impact per parameter change while BVL and BUL provided similar larger relative impact change.
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Figure 3: Cradle-to-gate GWP impact based on changes in design parameters including P&EL, BVL and BUL pairs (for a 14 layer pre-preg board). BUL 1 shown in grey and BUL 3 show in black.
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Thus far, the results have examined the overall cradle-to-gate impact of PWB manufacture. It is also instructive to see how different steps in the process vary differently with the same design changes explored above. The individual contributors included in the cradle-to-gate the emissions are: 1) the laminate materials and production, 2) the PWB fabrication energy consumption, and 3) the embodied GWP emissions of process chemicals. An analysis of their contribution to the total emissions, shown in Figure 4, indicates that their % contribution to emissions changes as a function of the design parameters explored, but more significantly between the impact of HDI and CM. Figure 4a explores these changes in percent contribution 1 BVL pair as a function of P&EL pairs versus Figure 4b which shows the percent contribution for a CM board. In each figure, the percent contribution to GWP changes as a function of P&EL pairs. For HDI, as the number of P&EL pairs increases, the laminate process contribution increases and the process chemicals and PWB fabrication percentage contribution decreases. In addition, the laminate process contribution is higher for fewer BVL pairs. For CM boards as the number of P&EL pairs increases, the laminate process contribution increases and the process chemicals percent contribution decreases. The scaling is different as the design parameters change because the impact scales differently with each of the design parameters chosen.
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Figure 4: (a) Percent contribution to the carbon dioxide emissions in PWB fabrication of an HDI board as a function of P&EL pairs for each major process step with internal BVL pairs of 1. (b) Percent contribution to the carbon dioxide emissions in PWB fabrication of a CM board as a function of P&EL pairs for each major process step with internal BVL pairs of 0.
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Figure 5 shows how the components of the HDI board itself contribute to fabrication energy consumption (green), embodied emissions associated with chemical use (red) and water consumption (blue). These pie charts are shown for two P&EL pair examples (2 and 6) as well as two BVL pair examples (1 and 2). A breakdown of the contributors to the emissions by each component shows that the largest contributor to all three metrics is the inner layer. Furthermore, for all three metrics, the contribution of the inner layer to the total increases as the number of P&EL pairs and BVL pairs increase. This is shown explicitly for all three metrics: fabrication energy consumption, process chemicals and water use. The inner layer contribution is most significant for the higher number of internal BVL pairs. Linking this result to the one described in Figure 3, the number of internal BVL pairs provides a significant lever in design change related to board impact.
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Figure 5: The percent contribution of the inner layer, outer layer, core layer and surface finish to the fabrication energy consumption, water consumption and embodied process chemicals emissions as the number of P&EL pairs and BVL pairs increase.
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The results presented thus far have focused only on assessing the embodied GWP emissions associated with chemicals use (as well as a brief mention of water consumption); however, using GWP-indicators to evaluate the environmental impact of process chemicals provides only limited information on the environmental and human health impacts of these chemicals. To provide another perspective on chemicals use, a chemical hazard assessment based on GreenScreen is included and relate this analysis to the design changes explored above. The GS-based benchmark results for HDI technologies are shown in Figure 6. More than 80% of the chemicals received a BM 1 or BM 2, which means that a large percentage of these chemicals exhibit various human health, environmental and/or physical hazard traits. Of the studied process chemicals, one is a BM 1 chemical, which is triggered by its confirmed carcinogenicity; one of the BM 2 chemicals is triggered by suspected mutagenicity and reproductive toxicity; several BM 2 and BM 3 chemicals are triggered by their Group II and Group II* Human Health hazard traits (see Table 1); four BM 2 chemicals are also triggered by environmental hazard traits; and three chemicals are triggered by physical hazards.
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Figure 6: The percentage of GS-based benchmark (BM) scores for chemicals in HDI manufacturing and the number of chemicals with their respective benchmark scores for each process step and by component within the board. Each process step is used differently within the five components, as shown in the table.
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One can also examine the benchmark scores for the chemicals used by step. Particularly, of the 14 steps, the pattern plating consumes the most chemicals, whereas routing and etching use the least. There is one BM1 chemical, which is used in the plated-through hole (PTH) step, an important step used in the processing of all of the components, except the surface finish layer. This process chemical is, however, used differently in each of the steps within these components such that its contribution to the total chemical use increases by approximately 3% for a buildup layer increase and by 8% for a buried via layer increase, but decreases by 9% for print and etch layer increases. Therefore, as environmental considerations are made in board design, addressing the use of the hazardous chemicals in the PTH steps would be an area where significant gains could be achieved if alternatives could be identified. Next steps for this work will include a more detailed exploration of the impact of the quantity of process chemicals on the CHA.
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CONCLUSIONS
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To the best of the authors’ knowledge, current life cycle inventory databases do not include estimates for HDI technology and understanding how this impact scales with relevant design parameters can help product engineers begin to consider environmental impact along with technical performance. This study, based on primary industry data, has shown that these design parameters can influence the final board impact for both CM and HDI PWBs. The analysis has shown that it continues to be important to explore more than just the facility based electricity use for electronic components: water and chemicals use are also areas for environmental impact reduction. Furthermore, by examining both the GWP impact from a gate-to-gate life cycle assessment perspective as well as assessing chemical hazard, the authors have begun work towards a more integrated evaluation approach for chemical-intensive processes. In particular this multi-metric assessment has indicated which processing steps within the HDI process would have higher hazard relative to others.
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The authors also outline a few areas of future work (besides the CHA work mentioned above). The results of this study are based on just a few board manufacturing facilities, so a more
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comprehensive analysis should assess how the energy and water impact varies across facilities making similar technologies. The remainder of process chemicals should be included in further work and an analysis that lets designers weigh relative designs according to environmental impact would also be of interest. Two additional technologies that the industry has cited that may further reduce environmental impact towards cleaner production of PWBs include: solid paste vias that eliminate several steps including electroless copper, de-smear, panel plate and pattern plates in all but one lamination step. This would likely further reduce global warming potential. In addition, there has been a trend towards greater use of films and liquid dielectrics including thermoplastics that would further reduce the need for copper. PWB manufacture is one of the more distributed steps in electronics production so likely significant variation exists. As the energy efficiency in the use phase of electronic devices continues to go down, it will be important to further investigate the impact of component and materials manufacture.
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ACKNOWLEDGEMENTS
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The authors acknowledge the contributions of Happy Holden and Joseph Smetana. The authors acknowledge support from the Government of Portugal through the Portuguese Foundation for International Cooperation in Science, Technology, and Higher Education, and a portion of this work was undertaken in the MIT Portugal Program. The authors also acknowledge partial support provided by the University of California Irvine PhD Bridge Program.
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Highlights We analyze global warming, water, and chemical use for printed wiring boards Variation of impact with design of high density interconnect boards Impact driven by energy to fabricate boards and board laminate materials