Improving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model

Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

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Robotics and Computer-Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim

Improving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model Tsung-Nan Tsai n Department of Logistics Management, Shu-Te University, 59, Hun Shan Road, YenChau, Kaohsiung 82445, Taiwan, ROC

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 March 2009 Received in revised form 22 September 2010 Accepted 13 January 2011 Available online 12 February 2011

This paper presents industrial applications for improving the capability of the fine-pitch stencil printing process (SPP) based on the DMAIC framework and using Taguchi-based methodologies. SPP is widely recognized as the main contributor of soldering defects in a surface mount assembly (SMA). An inadequate volume of solder paste deposition or poor printing quality can cause soldering defects and lead to significant reworking and repairing costs. In practice, both the desired amount of solder paste volume (quantitative index) and printing quality (qualitative index) are preferably used to monitor the SPP for the reduction of soldering defects during the statistical control process (SPC), particularly for a fine-pitch solder paste printing operation. To continuously improve SPP capability, the DMAIC framework is followed and Taguchi-based methodologies are proposed under the considerations of single characteristic performance index (SCPI) and multiple characteristic performance indices (MCPI). The SCPI is optimized using the conventional Taguchi method. Then, a Taguchi fuzzy-based model is developed to optimize the SPP with the MCPI property. Optimizing a multi-response problem by the Taguchi method involves the engineer’s judgment which tends to increase the degree of uncertainty. The performance of these two approaches is compared through the process capability metric, and the material and factors significantly affecting the fine-pitch SPP performance are reported. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Surface mount technology Stencil printing Taguchi method Process capability Fuzzy inference system

1. Introduction 1.1. Surface mount assembly Surface mount technology (SMT) is a significant development in the electronics industry and is now used to fabricate many types of electronics products. The surface mount assembly (SMA) process is comprised of solder paste stencil printing, component placement (Pick and place, P&P), and solder reflow process steps. A squeegee is employed in the stencil printing process (SPP) to deposit solder paste into the stencil openings and to leave the desired amount of solder paste on the PCB pads where the SMCs will be placed. The entire assembled board is heated in a reflow oven to form strong solder joints between the PCB pads and the SMCs. The SMT production yield becomes increasingly important to fabricate more reliable modern 3C products because the products’ functional scopes are more diversified and sophisticated. This issue is particularly important in producing telecommunication products, due to the reduced size of the device, extended board complexity, and increased product functionality.

n

Tel.: +886 7 6158000x4511; fax: +886 7 7801946. E-mail address: [email protected]

0736-5845/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.rcim.2011.01.002

To improve the overall SMT production yield, much efforts have been devoted to enhancing the process capability, particularly for SPP [1,2]. Studies have reported that approximately 60% of soldering defects originate from poor SPP performance [3,4] because of nonlinearity. The multiple performance characteristic indices (MCPI) involved are shown in Fig. 1. Controlling the amount of solder paste deposits can prevent the failure of solder joints during the statistical control process (SPC) activity [2]. The closer the amount of solder paste is to a nominal value, the better the stencil printing performance. The presence of printing defects (such as bridges, slumping, incompleteness, or shifting) also has a significant influence on the SPP performance and solderability [5]. If the printing defects occur they will potentially lead to soldering defects during the downstream processing stage. For example, incomplete printing defects can cause solder voids in the solder joint, whereas the bridge and slump prints can cause ‘‘short’’ solder joints after the solder reflow process. These increase productivity and quality losses which can lead to the necessity of significant cost expenditure later on to correct the soldering defects through extra reworking and repairs [6,7]. The smaller the product design, the greater the pin-count of the components on the PCB, and the poorer the stencil printing performance will be. Therefore, improvement of fine-pitch stencil printing capability is necessary so as to increase first-pass yields and product reliability, supply conforming parts to other downstream

T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

809

Qualitative index – Printing quality Standard

Bridge

Slump

Incomplete

Shift

Solder paste volume

Pad Laser

H W

Area Solder paste height

Quantitative index – solder paste volume

Fig. 1. Multiple quality characteristics of SPP (Compiled from Lathrop [31]).

stages, and thus reduce manufacturing costs. In recent years, manufacturers and researchers have attempted to ascertain the desired stencil printing control factors for improving the SPP performance based on their accumulated field experience, operation manuals and failure symptoms. Bao et al. [8] reported the rheology of the solder paste, particle size in the paste, and its distribution all affect the consistency of the printing pattern and the performance. Owczarek and Howland [9] revealed a strong connection between the snap-off distance and the volume of solder paste deposited. Pan et al. [10] declared the aperture area and stencil thickness to be the two SPP variables having the most significant influence on obtaining the appropriate amount of solder deposition. On the other hand, Whitmore et al. [11] declared the volume of the solder paste deposited to be unconnected to the designated stencil thickness. Mannan et al. [12] observed that the height of the paste deposited is proportional to the printing speed during the stencil printing process. Apparently controversies and conflicting results exist between the various studies. Besides, researchers have usually focused on how to optimize a single quality or property using various methods and have not provided a long-term SPP control framework. Studies include those on artificial intelligence [7], the Taguchi analysis method [13], and data mining [14]. Furthermore, these research results may involve mathematical complications that make it difficult for the process practitioners to have prerequisite knowledge, although proportional improvement is considered significant. 1.2. Fuzzy inference system Fuzzy set theory has been widely applied to solve problems involving vague expert knowledge, uncertainty or imprecise data [15]. Fuzzy inference systems (FIS) derived from fuzzy set theory can be used to easily transform the linguistic expressions of the experts into a rule base for the controlling of complex systems, and is suitable for formulating the relationship between system inputs and responses. An FIS is comprised of a fuzzifier, a fuzzy rule base, a fuzzy inference engine and a defuzzifier. The fuzzifier transforms the crisp (nonfuzzy) inputs into fuzzy sets using membership function (MBF). Currently there is no universally accepted criterion to properly define the shape of the MBF for the fuzzy subsets of the control variables. The Mamdani implication method allows easier interpretation of the relationship of inputs and responses to the fuzzy rule base, which

consists of a set of IF-THEN production rules. The fuzzy inference engine then performs the inference procedure to generate a fuzzy value based on IF-THEN rules. Finally the defuzzifier converts the fuzzy value into a crisp output. A typical fuzzy rule ‘‘IF (x is Ai and y is Bi) THEN (z is Ci)’’ used in a Mamdani inference system, can be implemented via a fuzzy relation Ri with membership function mRi, as defined in Eq. (1):

mRi ¼ mðAi and Bi -Ci Þ ðx,y,zÞ ¼ ½mAi ðxÞ and mBi ðyÞ-mCi ðzÞ,

ð1Þ

where Ai and Bi is a fuzzy set Ai  Bi in X  Y; Ri ¼(Ai and Bi)-Ci is a fuzzy relation in X  Y  Z; and (-) represents the fuzzy implication function. Considering the simplest fuzzy rule, ‘‘IF (x is Ai) THEN (y is Bi)’’, the MBF, mRi is shown in Eq. (2):

mR ¼ minfmA ðxÞ, mB ðyÞg,x A X,y A Y:

ð2Þ

Given the relation R, of X to Y where the fuzzy set for X is denoted by A0 , the fuzzy set for Y, denoted by B0 is inferred from A0 . Based on the Mamdani implication method of inference reasoning, the aggregated output for the set of disjunctive rules using the max-min compositional operation is

mBu ðyÞ ¼ maxfminðmA’ ðxÞ, mR ðx,yÞÞg,x A X,yA Y:

ð3Þ

The center-of-area (COA) defuzzification method is frequently used to compute the weighted average of the MBFs. Assuming a discrete universe of discourse, the crisp output Z is produced by calculating the COA of consequence for fuzzy subsets according to Eq. (4): Pm z m ðz Þ Z ¼ Pi ¼m 0 i C i , ð4Þ i ¼ 0 mi ðzi Þ where m is the number of quantization levels of the output; zi is the amount of output at the quantization levels I; and mi(zi) represents its membership value in C. 1.3. Taguchi method The Taguchi method is a cost-effective quality improvement methodology which has been widely applied in a variety of industries for the purpose of achieving robustness in manufacturing processes and design [16–19]. The Taguchi method provides a systematic scheme for determining the effects of various factors and their possible interactions. The results can help to design a

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process for achieving the particular output and quality characteristics, as well as uncover important factors that have an influence on the qualities and characteristics of interest [16]. Two important tools provided by the Taguchi design are the orthogonal array (OA) and the signal-to-noise (S/N) ratio. The OA can be used to control experimental errors, so as to allow engineers to study many parameters of influence simultaneously, in order to better estimate the effects of independent factors and their interactions. In the Taguchi design both the mean of the performance characteristic and its variance are considered. The S/N ratio is employed as a performance measure for evaluating the robustness and variation of a system [20]. A higher S/N ratio represents better system performance and lower variation. Three types of S/N ratios were classified by Taguchi [21], based on the quality characteristics of the response: (1) the smaller-the-better (STB); (2) the larger-the-better (LTB); and (3) the nominal-the-best (NTB), as shown in Eqs. (5), (6), and (7), respectively:  X n 1 S=NSTB ðZÞ ¼ 10 log10 y2 , ð5Þ n i ¼ 1 ij S=NLTB ðZÞ ¼ 10 log10

S=NNTB ðZÞ ¼ 10 log10

 X n 1 1 , n i ¼ 1 y2ij



m2 s2

ð6Þ



y1 þ y2 þy3 þ    þyn where m ¼ n Pn 2 2 i ¼ 1 ðyi yÞ : s ¼ n1

and (5) the relative weights and ranking of the decision alternative are synthesized. In AHP, reciprocal relationships are adapted so as to compare m elements under given conditions, and the response is transformed into a nine-point scale. The results of the pairwise comparisons are constructed as a judgment matrix. The maximum values, lmax, are then calculated. The consistency index (CI) of the judgment matrix can be calculated using Eq. (8): CI ¼

lmax m m1

:

ð8Þ

The consistency ratio (CR) allows us to measure the consistency of the pairwise comparisons, as depicted in Eq. (9). A CR of 0.1 or less indicates a consistent judgment [26] CR ¼

CI : RI

ð9Þ

The random index (RI) is the consistency index for a randomly generated reciprocal comparison matrix based on the ninepoint scale. Applications of AHP have been dominant in manufacturing and other fields [27]. The printing quality is judged subjectively by human-inspectors with/without the aid of an automated visual inspection (AVI) system. The AHP method is suitably employed in order to derive the relative weight of each printing defect subjectively judged by engineers according to the severity of PCB functionality. 1.5. Six sigma management and DMAIC

ð7Þ

In the application of a Taguchi design, the main focus is on improvement of the product and the process [22–24]. However, most previous applications of the Taguchi method have generally emphasized optimization of a single response. Consequently simultaneous optimization solutions on the MCPI property have been scarce. The optimization of an SPP involving multi-response characteristics, including the desired amount of solder paste deposited (quantitative index) and the elimination of printing defects (qualitative index). Optimizing the multi-response problem using the Taguchi method involves the engineer’s judgment which often increases the degree of uncertainty [20]. Traditionally this sort of problem has been solved by assigning a weight for each response. However, the weighted S/N ratio does not agree with the viewpoint of the Taguchi quality loss function. Therefore, we alternatively converted a multi-response index into a single synthesis performance index using an FIS to investigate relationships between the multiple responses and the SPP performance and this way determine the efficiency of each parameter in the design of the Taguchi experiment. 1.4. Analytic hierarchy process The analytic hierarchy process (AHP) [25] provides an intuitive multi-criteria decision-making framework that allows decisionmakers to transform subjective judgments into objective measures, for the solving of complex and subjective decision-making problems based on the principles of decomposition, comparative assessment and synthesis of priorities. Typically a five-step procedure is used for tackling multi-criteria decision-making or ranking problems: (1) the problem is discomposed into a hierarchy of decision elements; (2) inputs are gathered through a pairwise comparison of decision elements; (3) the consistency of the judgment matrix is verified using a consistency ratio (CR); (4) the relative weights of the decision elements are calculated;

Six-sigma quality management strategy follows the methodology inspired by Deming’s Plan-Do-Check-Act (PDCA) Cycle, which has been widely implemented in many industries. Six-Sigma uses a set of quality management tools to identify and remove the causes of defects and errors in both manufacturing and business processes. The acronym Define-Measure-Analyze-Improve-Control (DMAIC) is used for projects aimed at improving an existing process. The basic DMAIC deployment roadmap consists of the following five steps [28]: (1) Define: define the related improvement goals pertaining to customer demands and the enterprise strategy. (2) Measure: measure the current process capability and collect relevant data. (3) Analyze: analyze the process data to ascertain the cause-andeffect relationships of interest, and ensure that all system factors have been considered. (4) Improve: optimize the process based on data analysis using DOE, process capability analysis, and other statistical techniques. (5) Control: conduct pilot runs to achieve desired process capability and make advances to the mass production, establish long-term process control mechanisms, and execute continuous monitoring of the process. The remainder of this paper is organized as follows. In Section 2 the proposed methodology is outlined, followed by a step-bystep discussion. Section 3 discusses the findings and results. The concluding remarks are presented in Section 4.

2. The proposed methodology In this study, we work with the DMAIC framework to improve the SPP capability, as shown in Fig. 2. This main effort is to reduce variation in the volume of solder paste deposits from a nominal value and eliminate printing defects under the considerations of

T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

Collect statistical process control data

811

Perform Gage R&R study

M

Analyze control chart and process capability

Determine critical to quality (CTQ) of SPP

Database

A

Rule base

Fuzzy knowledge base

Determine key SPP factors for investigation

•Solder paste volume •Printing quality

Method #2

Fuzzy inference engine

Fuzzification

Defuzzification

Fuzzy inference system

Design and conduct a L18(2 1×37) experiment

Multiple characteristics Performance indices (MCPI)

•Solder paste Method #1 volume

Taguchi method – SCPI optimization

Taguchi fuzzy-based modelMCPI optimization

I

Confirmation test, and performance comparisons

C

Long-term process control Fig. 2. Roadmap for SPP optimization and capability improvement.

Data analysis and the calculation of processing capability can be error-prone without a precise measurement system [23]. In the ‘‘measure’’ phase, the SPC data was collected for further process capability analysis. A gage repeatability and reproducibility (Gage R&R) study for an automated optics inspection (AOI) is performed to minimize the variability in the measurement system and operators before the experiment was launched. In the experiments, the volume of solder paste was measured with an AOI system, and printing defects were identified based on the subjective judgment of human-inspectors using the automated visual inspection (AVI) system. Ten bricks of solder paste deposits were measured with three replications and four operators. The results of the Gage R&R study are shown in Table 1. The operators exhibit a consistent measuring stability and reproducibility. The total Gage R&R accounts for 15.32% of the study variation ( o30%) showing that the AOI system is acceptable for the purpose of distinguishing 9 types of parts and to adequately categorize said parts. Fifteen PCBs with five thin mall outlined package (TSOP) (lead-pitch: 0.5 mm) components on each board were sampled. The volume of solder paste deposits was measured for each component (three samples each) (15  3¼ 45). Thereafter, the mean (x) and R control charts were constructed and are shown in Fig. 3. In this figure, the lower control limit (LCL), center line (CL), and upper control limit (UCL) are computed to be 5356.6, 5762.2 and 6168.0 mil3, respectively. The LCL, CL, UCL control limits of the R-chart are 0.0, 397.0, and

Source

StdDev (SD)

Study var (6 SD)

Study var (SV) (%)

Total gage R&R Repeatability Reproducibility Part-to-part Total variation

21.845 21.845 0.000 140.924 142.607

131.073 131.073 0.000 845.545 855.644

15.32 15.32 0.000 98.82 100.00

Note: number of distinct categories (NDC) ¼ 9.

Xbar-R Chart Sample Mean

2.1. Instrument accuracy

Table 1 Summary of the data for the Gage R&R study.

UCL=6168.0

6200 6000 5800 5600 5400

_ X=5762.2 LCL=5356.4 1

Sample Range

single and multiple performance indices. The sub-goals include the following: (1) comparing the optimization performance attained by the Taguchi method and Taguchi fuzzy-based model; (2) identifying the factors having significant effects on the fine-pitch SPP performance; and (3) enhancing the SPP capability for fine-pitch SMCs with minimal defects. The ‘‘define’’ phase of DMAIC is outlined in this section and Section 1.1. The other phases are described in the following subsections.

2

3

4

5

6

7 8 9 10 11 12 13 14 15 Sample UCL=1021

1000 750 500 250 0

_ R=397 LCL=0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15

Sample Fig. 3. XR control charts for stencil printing process status.

1021.0 mil3. Viewing the control charts, it can be seen that the process has an ‘‘in-control’’ status. The SPP capability analysis is shown in Fig. 4. The upper specification limit (USL), Target and lower specification limit (LSL) for 0.5 mm pitch printing process employed by the subject company are 65003, 5850 and 5250 mil3 for day-to-day process control. The Cp computed for the initial stage (with a process

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mean (x) of 5762.2 mil3) is 0.89 and Cpk is calculated to be 0.73. Notably both the Cp and Cpk values show that stencil printing capability is not sufficient to meet industrial requirements. 2.2. Analysis and improvement Proper selection of the factors and factor levels used in the experiment is an important issue during the ‘‘analyze’’ phase if one is to achieve the appropriate design space. The performance of stencil printing is influenced by the geometric properties of the stencil design, stencil printer, characteristics of the solder paste, the product configuration, and the printing parameter settings [1]. As indicated in the literature review in Section 1.1, there is no rule-ofthumb to help selecting key SPP factors. There is a dearth of relevant knowledge and a number of controversies and conflictions among previous studies. Accordingly the selection of process factors for robust process design and optimization of fine-pitch SPP is Stencil Printing Process Capability Analysis LSL

Target

USL Within Overall

Process Data LSL 5250 T arget 5850 USL 6500 Sample Mean 5762.18 Sample N 45 StDev(Within) 234.285 StDev(Overall) 326.599

Potential (Within) Capability Cp 0.89 CPL 0.73 CPU 1.05 Cpk 0.73 Overall Capability Pp 0.64 PPL 0.52 PPU 0.75 Ppk 0.52 Cpm 0.59

00 00 00 00 00 00 00 00 50 52 54 56 58 60 62 64 Fig. 4. Capability analysis for SPP.

substantially critical. Various factors of influence must be considered in the design of a robust fine-pitch solder paste deposition process. We considered a continuum of factors from previous studies [9,10,12] that impact the measured characteristics, as well as the expert knowledge of the experienced process practitioners who work for company V. Important input factors taken into consideration are shown in Fig. 5 in red. In the ‘‘improve’’ phase, the experimental design is used to reduce SPP variation and move the design towards the desired printing performance. Full factorial experimental design requires a number of experimental runs which makes the process timeconsuming and cost-ineffective. To effectively ascertain the key stencil printing factors, a fractional factorial experimental design uses a portion of the full factorial columns to estimate the main factor effects and their interactions. The settings for each printing factor are shown in Table 2. Factor (A) has two levels (component lead-pitch), whereas seven other factors (B, C, D, E, F, G, and H) each possess three levels. The initial factor level combination is B2C2D2E2F2G2H2. The Taguchi method relies on the assignment of factors into a specific OA to determine which test combinations use minimal resources, thereby reducing the experimental design complexity. For example, an examination of eight factors (21  37) requires a total of 4374 trials. Taguchi OA L18 is selected due to fitting the experimental requirements and only needing 18 experimental runs. A three-level L18 (21  37) orthogonal array with eight factors is employed in order to maintain the current process settings in the middle level and improve SPP capability. The optimal stencil printing parameters are determined by examining the experimental data acquired by using the OA. The stencil printing factors and the levels considered in the experiment are shown in Table 2. Three customized laser-cut stainless stencils were selected with different stencil thicknesses (1.0, 1.2, and 1.5 mm) and aperture sizes (based on pad area ratio, 85%, 100%, and 115%),

Type

Aperture design

Stencil design Thickness Manufacturing Type process Printing parameters

Squeegee

Down-stop speed

Wiping Working frequency environment

Stencil printing factors

Machine setup

Product configuration

Snap-off Squeegee speed distance Density

Squeegee pressure

Alignment accuracy

Component Types Particle shape

Lead-pitch

Thixotropy

Supplier

Solder powder Particle distribution Type Viscosity

Solder paste

Solids content

Storage

Flux Flux content

Solvent evaporation rate

Fig. 5. Stencil printing factors (for interpretation of the references to color in this figure, the reader is referred to the web version of this article).

T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

813

Table 2 Stencil printing parameters and levels used in the experiment. Stencil printing parameters

Level I

Level II

A: lead-pitch (mm/mils)

0.4/16

0.5/20

B: paste particle size (type) C: squeegee pressure (bar) D: squeegee speed (mm/s) E: snap-off height (mm) F: stencil aperture area (%) G: stencil thickness (mm) H: paste viscosity (kcps)

 200+ 325 mesh (II) 1 20 0 85 1 800

 325 + 400 mesh (III) 3 40 1 100 1.2 1050

Level III

Descriptions

 400 + 500 mesh (IV) 5 60 2 115 1.5 1300

Distance between the centers of the two component leads in the IC package Diameter of the solder paste particle Amount of air pressure applied on the squeegee Squeegee traveling speed Distance between the PCB and the stencil Ratio of stencil aperture and area of pad pattern Thickness of the laser-cut stencil Solder paste viscosity

Note: each initial factor level is identified by an underscore.

1005 R

Table 3 Summarized weighting data for evaluating printing quality.

R1 Fiducial

1005 C

15.4 mm

U1 SQFP-128P P=0.4

15.4 mm

Fiducial mark U2

U3 SOIC-32 P=0.65

SOIC-32 P=0.65

Tolling Hole

TSOP-32 P=0.5 TSOP-32 P=0.5

Fig. 6. Customized stencil.

as shown in Fig. 6. We particularly consider the impact of the solder paste transfer rates as derived from the various combinations of stencil thickness and aperture area. Some of the noises in this experiment arise from (1) working temperature and humility (2) variations in the stencil printer; and (3) dust. The focused printing defects are shown in the top part of Fig. 1, which include bridge, slumping, incompleteness and shifting, and are subjectively identified by human-inspectors using AVI equipment. The relative weight for each type of printing defect was ranked by experienced engineers based on the severity of its impact on PCB functionality using the AHP method, as shown in Table 3. Note that a CR value of less than 0.1 indicts a consistent judgment is obtained. For example, there are bridge and shifting defects in the experimental run, the printing quality score is (1-0.261-0.119)  100¼62 calculated using a hundred-point scale (multiplied by 100). The experiments were conducted in a random order to eliminate bias. The responses used for further analysis included the volume of the solder paste deposits and printing quality score. Three repetitions were performed for each experimental run. The amount of solder paste deposit was measured at the four corners of the shrink quad flat pack (SQFP) and TSOP packages. Hence, eight solder paste bricks were measured for SQFP and four bricks for TSOP. The S/N ratios were computed for both quantitative (solder paste volume—Z1) and qualitative (printing defect—Z2) indices for each of the 18 runs, are given in Table 4. The solder paste volume is an NTB type quality characteristic (calculated using Eq. (9)) because different lead-pitch printing processes require different amounts of solder paste during deposition, whereas the printing defect is an LTB type quality characteristic (calculated using Eq. (8)). Greater S/N ratios correspond to a better printing performance. 2.2.1. Statistical analysis For statistical analysis of the DOE, the analysis of variance (ANOVA) was performed to determine the significance of each

Alternative

Bridge

Slump

Incomplete

Shift

(CR¼ 0.02) Priority

0.261

0.169

0.451

0.119

factor in terms of its effects on the quality characteristic(s). The ANOVA analysis branches the total effect of the averaged output quality characteristic and variability to each factor in the OA. The significance test is based on the F distribution, which is a ratio of the degrees of freedom (DoF) for the factor divided by the DoF of the experimental error. Taguchi suggested adopting percentage contribution (r%) to evaluate the factor’s degree of importance on the quality characteristic, which can be expressed as

rð%Þ ¼ SSFu =SST  100%,

ð10Þ

where SS’F ¼ SSF Verror  DoF is the modified sum of squares for each factor; and SST is the total sum of the squares for each factor. In the preliminary ANOVA analysis, the factors paste particle size (B), squeegee speed (D), and snap-off height (E) are insignificant since their F ratios have a confidence significance of less than 95%. Consequently these three factors are pooled and considered error terms. The summary of the ANOVA analysis of the S/N ratios and the results is shown in Table 5. The component lead-pitch (A), stencil aperture area (F), and stencil thickness (G) account for approximately 83.66% of the total variation in the SPP. Specifically the lead-pitch factor represents about 55% of the variation. This indicates that the product configuration and stencil design have a significant effect on the amount of the solder paste deposits and the overall printing performance considering the single characteristic performance. 2.2.2. Optimizing SCPI of stencil printing using the Taguchi method The conventional Taguchi method is employed in order to optimize the SPP with a single response using the experimental results shown in Table 4. The main effects of the S/N ratios and mean are shown in Fig. 7(a). The variation is reduced by selecting the factor levels in order to maximize the S/N ratios. During ANOVA analysis of the S/N ratios, the main contributors are factors A, F, and G. The ‘‘A’’ factor (lead-pitch) is categorized into 0.4 mm (ultra-fine-pitch) and 0.5 mm (fine-pitch) printing processes which identify the different lead-pitch printing requirements. Consequently for the 0.4 mm lead-pitch process, the optimal combination of factor levels will be A1C3F3G3H3. For the 0.5 mm lead-pitch process, the optimal combination of factor levels will be A2C3F3G3H3. To improve SPP capability, two important parts need to be focused upon: (1) reducing the variation of solder paste deposits;

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Table 4 Experimental results and performance evaluations. Exp. run

Control factors

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Solder paste volume (quantitative)

A

B

C

D

E

F

G

H

yi1

yi2

yi3

yi

si

Z1 (dB)

vj1

vj2

vj3

vj

sj

Z2 (dB)

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 2 3 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2

1 2 3 2 3 1 1 2 3 2 1 2 3 1 2 2 3 1

1 2 3 2 3 1 3 1 2 2 3 1 1 2 3 3 1 2

1 2 3 3 1 2 2 3 1 2 3 1 3 1 2 1 2 3

1 2 3 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1

3255 2325 2945 2635 4185 2480 3875 2015 2790 6045 5735 4185 4960 6510 6355 7250 5425 5270

3354 2298 3043 2595 4201 2467 3910 2098 2856 5945 5784 4089 5089 6477 6262 7209 5380 5243

3248 2409 3023 2646 4156 2511 3777 2139 2734 5978 5701 4209 4912 6539 6401 7289 5445 5290

2582.7 3072.7 3654.0 3447.3 3135.7 2895.0 3664.7 3379.3 3247.0 5869.0 6480.7 4907.0 5438.7 5262.3 6263.3 5963.3 5542.3 6030.0

46.76 50.16 32.05 37.07 48.88 37.80 36.61 43.32 48.14 57.97 46.20 50.57 45.61 44.84 47.06 50.95 55.05 42.72

34.84 35.74 41.14 39.37 36.14 37.68 40.01 37.84 36.58 40.11 42.94 39.74 41.53 41.39 42.48 41.37 40.06 42.99

100 83 100 100 88 55 88 55 100 100 83 43 100 100 100 83 100 100

100 74 83 74 83 38 88 55 83 100 74 43 100 83 88 100 74 88

88 88 100 55 100 62 100 43 100 88 88 55 88 88 83 74 55 83

96.00 81.67 94.33 76.33 90.33 51.67 92.00 51.00 94.33 96.00 81.67 47.00 96.00 90.33 90.33 85.67 76.33 90.33

6.93 7.09 9.81 22.59 8.74 12.34 6.93 6.93 9.81 6.93 7.09 6.93 6.93 8.74 8.74 13.20 22.59 8.74

39.60 38.17 39.39 36.90 39.04 33.69 39.23 33.97 39.39 39.60 38.17 33.27 39.60 39.04 39.04 38.46 36.90 39.04

Table 5 ANAVA analysis results for S/N ratios. Source

DoF

SSF

Adj MS

F ratio

SS’F

A C F G H Residual error Pooled error Total

1 2 2 2 2 2 6 17

61.432 3.523 13.106 21.053 4.977 0.411 4.891 109.393

61.432 1.761 6.553 10.527 2.488 0.206 0.815

75.36 2.16 8.04 12.91 3.05

60.62 1.89 11.48 19.42 3.35

r (%) 55.41 1.73 10.49 17.75 3.06 11.55 100.00

42 41 40 39 38 37

A

B

C

D

E

F

G

H

A1 A2 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 F1 F2 F3 G1 G2 G3 H1 H2 H3

S/N ratio (dB)

Note: factors B, D, and E are pooled into error term.

Factor levels

6000 5500 5000 4500 4000 3500 3000

A

B

C

D

E

F

G

H

A1 A2 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 F1 F2 F3 G1 G2 G3 H1 H2 H3

Solder paste volume (mil3)

Solder paste printing quality (qualitative)

Factor levels Fig. 7. Main effects plotted for S/N ratio and mean.

and (2) to adjust the process mean so that it is close to the nominal (target) value. Fifteen PCBs with two TSOP (lead-pitch: 0.5 mm) and one SQFP components (lead-pitch: 0.4 mm) on each

board are utilized for the confirmation experiments. Experimental process conditions include using C3F3G3H3 for both the 0.4 (A1) and 0.5 mm (A2) pitch printing processes for consideration of the SCPI (solder paste volume). The volume of solder paste bricks was measured for each component. The analysis results for the original and the optimum settings shown in Table 6 show that the S/N ratios improved for both the 0.4 and 0.5 mm cases at 5.11 dB. The mean solder paste volume is increased by 21.2% and 11.7% for the 0.4 and 0.5 mm lead-pitch printing processes, respectively. The closer the solder paste volume is to the nominal value, the better the SPP performance. The mean of the solder paste volume must be adjusted to agree with the nominal value. According to Fig. 7(b), the solder paste volume increased monotonically with respect to factors F (stencil aperture size) and G (stencil thickness). Thus, another confirmation experiment was conducted using the setting C3F2G2H3 and S/N ratio (42.31 dB) to obtain the mean (5819 mil3). The mean approached the CL value (5850 mil3) that is used by the subject company (company V). Nevertheless, for the 0.4 mm pitch printing process, the optimum settings needing to acquire the appropriate amount of solder paste deposits should be kept at A1C3F3G3H3 which is closer to the nominal value (CL)—4850 mil3 used by the subject company. For the mixed lead-pitch printing process (i.e., PCB is designated with 0.4 and 0.5 mm pitch components), larger stencil apertures (115% pad area) and a 1.2 mm thick stencil are needed for the 0.4 mm lead-pitch components, to deposit the proper amount of solder paste, whereas stencil apertures with a 115% pad area and a 1.0 mm thick stencil are suggested for 0.5 mm leadpitch components in order to deposit the appropriate amount of solder paste.

2.2.3. Optimizing MCPI using the Taguchi fuzzy-based model A Taguchi fuzzy-based model is developed to optimize the stencil printing process with the MCPI properties. The experimental results (shown in Table 4) are used for the analysis. The S/N ratios for the solder paste volume are found to be between 34.84 and 42.94 dB. The S/N ratios for solder paste volume are lower for experimental runs 1 and 2 have lower and higher printing quality, respectively. This indicates that the volume of solder paste deposits is insufficient, but a good shaped brick is formed. However, the stencil printing performance is still doubtful in its ability to obtain good solder joints after the solder reflow process.

T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

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Table 6 Predicted results for original and optimum settings. Lead-pitch

A1 (0.4 mm pitch)

Settings

B2C2D2E2F2G2H2 (original)

C3F3G3H3 (1st optimized)

B2C2D2E2F2G2H2 (original)

C3F3G3H3 (1st optimized)

C3F2G2H3 (2nd optimized)

S/N ratio (dB) Mean (mil3)

36.42 4116

41.53 4778

40.11 5536

45.22 6198

42.31 5819

A2 (0.5 mm pitch)

Table 8 Multiple characteristic performance indices (MCPI). Run (no.)

I-S/N

V-S/N

MCPI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

0.198 0.470 0.845 0.003 0.912 0.066 0.942 0.111 0.967 1.000 0.775 0.000 1.000 0.912 0.912 0.820 0.573 0.912

0.000 0.110 0.772 0.555 0.159 0.348 0.634 0.368 0.213 0.646 0.993 0.601 0.820 0.803 0.937 0.800 0.640 1.000

0.078 0.282 0.895 0.333 0.500 0.209 0.780 0.221 0.213 0.788 0.898 0.361 0.938 0.923 0.950 0.904 0.624 0.950

Fig. 8. Membership functions of inputs and MCPI. Table 9 ANOVA analysis results for MCPI S/N ratios.

Table 7 Fuzzy rules for multiple performance indices. Stencil printing process: MCPI

Normalized solder paste volume VL (quantitative) L M H VH

Normalized printing quality (qualitative) VL

L

M

H

VH

T VS S SM M

VS S SM M ML

S SM M ML L

SM M ML L VL

M ML L VL H

Source

DoF

SSF

Adj MS

F ratio

SS’F

A D E F G H Residual error Pooled error Total

1 2 2 2 2 2 2 4 17

296.205 46.895 25.474 216.51 61.676 29.709 2.539 21.394 700.403

296.205 23.448 12.737 108.255 30.838 14.855 1.270 5.349

55.38 4.38 2.38 20.24 5.77 2.78

290.857 36.198 14.777 205.813 50.979 19.012

r (%) 41.53 5.17 2.11 29.38 7.28 2.71 11.82 100.00

A triangular MBF is the easiest way to approach the convex function and the simplest way to explain system behavior [29]. In the MCPI optimization strategy, the S/N ratios are first fuzzified using triangular MBFs. Five fuzzy subsets are assigned for the two inputs (The S/N ratios for both solder paste volume and printing quality are normalized as two input variables), including very_low (VL), low (L), medium (M), high (H), and very_high (VH), as graphically depicted at the top of Fig. 8. Nine fuzzy subsets are assigned for output (MCPI), including Tiny (T), Very_small (VS), Small (S), Small_medium (SM), Medium (M), Medium_large (ML), Large (L), Very_large (VL), and Huge (H), as shown on the bottom of Fig. 8. Twenty-five possible fuzzy rules (see Table 7) are derived directly according to the criterion that the larger the S/N ratio, the better the performance characteristic will be. Next, the inference engine performs a fuzzy reasoning on the fuzzy rules to generate a fuzzy value. The fuzzy reasoning of these rules then yields a fuzzy output using the max-min compositional operation [30]. Finally the defuzzifer converts the fuzzy value into a crisp

A1 A2 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 F1 F2 F3 G1 G2 G3 H1 H2 H3

Note: factors B and C are pooled into error terms.

-1 -3 -5 -7 -9 -11 Fig. 9. Main effect plots for S/N ratios of MCPI.

synthesis index (MCPI) using the COA defuzzification method, as shown in Table 8. The greater the MCPI value, the more optimal the combination of SPP parameters obtained is (under the considerations of solder paste volume and printing quality). The S/N ratios for MCPI computed for each level of the factors are shown in Table 9. The component leadpitch (A) and stencil aperture area (F) account for 70% of the total

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T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

variation in the SPP. The main effects for MCPI S/N ratios are shown in Fig. 9. The optimum parameter setting of 0.4 mm lead-pitch stencil printing process is A1D3E3F3G3H3, while the 0.5 mm pitch stencil printing process is set at A2D3E3F3G3H3. The factor levels for D, E, F, G, and H are identical and are considered simultaneously for the both 0.4 and 0.5 mm lead-pitch printing processes. Unfortunately the condition A2D3E3F3G3H3 for MCPI can produce excessive amounts of solder paste. This result is analogous to those found in a previous study considering the SCPI property, as discussed in Section 2.2.2. Fifteen PCBs (each having three SMCs) were utilized in the confirmation experiment under the conditionA2D2E3F2G3H3 to achieve appropriate solder paste volumes closer to the nominal value (5850 mil3). The results of the confirmation experiment for MCPI optimization are shown in Table 10. The S/N ratios for both the 0.4 and 0.5 mm cases improved at 9.51 dB. The optimum parameter settings gain improvement (6.22 dB) is comparable to the original setting for the 0.5 mm lead-pitch printing process. In addition, both the 0.4 and 0.5 mm lead-pitch printing processes can deposit more appropriate amounts of solder paste.

3. Results and discussions Following the DMAIC framework, the real SPC data collected from the subject company were analyzed and the SPP capability improved. In the SCPI optimization stage, we found that the main factors affect SPP performance are the component lead-pitch, stencil aperture ratio and stencil thickness. The conclusion of the factor analysis is identical to the previous study [10]. The

optimum parameter settings derived from this approach show that the S/N ratios improved for both the 0.4 and 0.5 mm leadpitch stencil printing cases at 5.11 dB. Two additional suggestions used to acquire the optimum volume of solder paste for different lead-pitch printing processes are as follows:

 The 0.4 mm lead-pitch printing process: A larger stencil aperture



(120% pad area), higher squeegee pressure (5 bar), larger snapoff distance (2 mm), thicker stencil (1.2 mm), and higher paste viscosity (1300 kcps) are recommended to make it closer to the nominal value. The mixed lead-pitch printing process: A larger stencil aperture (115% pad area) and a 1.2 mm thick stencil are needed for the 0.4 mm lead-pitch printing process to deposit the proper amount of solder paste, whereas stencil apertures with a 115% pad area and a 1.0 mm thick stencil are suggested for 0.5 mm lead-pitch printing instances.

In the MCPI optimization stage, the main factors lead-pitch, squeegee speed, snap-off distance, stencil aperture area, stencil thickness and paste viscosity are reported. The parameter settings to deposit appropriate amount of solder paste for both the 0.4 and 0.5 mm lead-pitch printing processes are as follows: a medium squeegee speed (40 mm/s), larger snap-off height (2 mm), larger stencil aperture (120% pad area) (1.0 mm), and higher paste viscosity (1300 kcps). All are recommended to approach closer to the nominal value. However, after the confirmation experiments, the condition (A2D2E3F2G3H3) can lead to improvement (6.22 dB) when compared to the original settings. The S/N ratios

Table 10 Summary of optimization performance for MCPI. Lead-pitch

A1 (0.4 mm pitch)

Settings

B2C2D2E2F2G2H2 (original)

D3E3F3G3H3 (1st optimized)

B2C2D2E2F2G2H2 (original)

D3E3F3G3H3 (1st optimized)

D2E3F2G3H3 (2nd optimized)

S/N ratio (dB) Mean (MCPI) Mean (volume) (mil3)

 8.82 0.29 4135

0.69 0.85 4822

 0.71 0.71 5505

8.80 1.28 6218

5.51 0.97 5835

6100 6000 5900

A2 (0.5 mm pitch)

x control chart UCL = 6065 CL = 5818 LCL = 5572 Cp = 1.463 Cpk = 1.33

UCL = 6078 CL = 5835 LCL = 5592 Cp = 1.485 Cpk = 1.38

5800 5700 5600 5500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 700 600 500 400

R control chart UCL = 621 CL = 241 LCL = 0 σ = 142.39

UCL = 611 CL = 237 LCL = 0 σ = 140.26

300 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Multi-characteristic index optimization Single characteristic index optimization Fig. 10. XR control charts and capability analysis.

T.-N. Tsai / Robotics and Computer-Integrated Manufacturing 27 (2011) 808–817

for both the 0.4 and 0.5 mm lead-pitch printing processes improved at 9.51 dB. Apparently the fact that the MCPI optimization performance is superior to that of the SCPI is confirmed. Both the S/N ratio and sample mean of the MCPI optimization stage are closer to the nominal value. We further generated xR control charts and calculated the process capability indices—Cp and Cpk, as shown in Fig. 10. Compared with the status for the mass production phase (see Fig. 3), the Cp improved from 0.89 to 1.463, and Cpk was upgraded from 0.73 to 1.33. The standard deviation for the SCPI optimization stage was reduced from 326.60 to 142.39. In the MCPI optimization stage, Cp and Cpk were enhanced to 1.485 and 1.38, respectively. The sample mean of the MCPI optimization is closer to the nominal value (5850 mil3). In the ‘‘control’’ phase of DMAIC, the x  R control charts should be regularly updated for long-term monitoring of the solder paste volume following the control limits indicated in Fig. 10.

4. Conclusions SMT has become the dominant process used in the electronics industry. Poor SPP performance can cause significant productivity and quality losses. A large variation in the solder paste volume from the nominal value and significant amount of printing defects have the potential to produce soldering failures that can significantly increase manufacturing costs to correct the defects. This is particularly evident in the fine-pitch printing process. To continuously improve the fine-pitch SPP capability, the DMAIC framework is followed. First the instrumental accuracy was confirmed through a Gage R&R study. An L18 (21  37) Taguchi OA was employed to collect structured process data. In the ‘‘improve’’ phase of DMAIC, Taguchi-based methodologies are proposed under the considerations of SCPI and MCPI properties. The optimization results show that the fine-pitch SPP is adequate with a Cp above 1.46 and a Cpk above 1.33 when using these two proposed methodologies. As a result, the optimization methodologies developed in this study are useful for improving both the SCPI and MCPI of fine-pitch SPP with minimal defects. Nevertheless, the optimization performance derived from the Taguchi fuzzy-based model is superior to that of the SCPI. We also found that the geometric design of the stencil is important. It is important to adjust the aperture size and thickness of the stencil so that the process mean approaches the nominal value. In addition, in the ‘‘control’’ phase of DMAIC, the x  R control charts can be regularly updated to perform long-term monitoring of solder paste volume for continuous improvement of the DMAIC cycle.

Acknowledgments The author would like to thank the subject company for providing related support and the National Science Council of the Republic of China, Taiwan for supporting this research under Contract no. NSC 97-2221-E-366-007. References [1] Itoh M. General information on solder paste—technical report. Tokyo: KOKI Company Limited; 2005.

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[2] Shina SG. Six sigma for electronics design and manufacturing. New York: McGraw-Hill; 2002. [3] Montgomery DC, Keats JB, Perry LA, Thompson JR, Messina WS. Using statistically designed experiments for process development and improvement: an application in electronics manufacturing. Robotics and Computer Integrated Manufacturing 2000;16:55–63. [4] He D, Ekere NN, Salam B, Rajkumar D, Jackson G. Monte Carlo study of solder paste microstructure and ultra-fine-pitch stencil printing. Journal of Materials Science 2003;14:501–6. [5] Kennedy J. A study of solder paste printing requirements for CSP technology. Soldering and Surface Mount Technology 2000;12:13–8. [6] Tsai T-N, Yang T. A neuro-computing approach to the thermal profile control of the second-side reflow process in surface mount assembly. Journal of Manufacturing Technology Management 2005;16(3):343–59. [7] Liukkonen M, Hiltunen T, Havia E, Leinonen H, Hiltunen Y. Modeling of soldering quality by using artificial neural networks. IEEE Transactions on Electronics Packaging Manufacturing 2009;32(2):89–96. [8] Bao X, Lee N-C, Raj RB, Rangan KP, Maria A. Engineering solder paste performance through controlled stress rheology analysis. Soldering and Surface Mount Technology 1997;10:26–35. [9] Owczarek JA, Howland FL. A study of the off-contact screen printing process—Part I: model of the printing process and some results derived from experiment. IEEE Transactions on Components, Hybrids, and Manufacturing Technology 1990;13(2):358–67. [10] Pan J, Tonkay GL, Storer RH, Sallade RM, Leandri DJ. Critical variables of solder paste stencil printing for Micro-BGA and fine-pitch QFP. IEEE Transactions on Electronics Packaging and Manufacturing 2004;27:125–32. [11] Whitmore M, Mackay C, Hobby A. Plastic stencils for bottom-side chip attach. Electronic Packaging and Production 1997;37:68–72. [12] Mannan SH, Ekere NN, Ismail I, Lo EK. Squeegee deformation study in the stencil printing of solder pastes. IEEE Transactions on Components, Hybrids, and Manufacturing Technology 1994;17(3):470–5. [13] Al-Refaie A. Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA. IEEE Transactions on Electronics Packaging Manufacturing 2009;32(4):256–64. [14] Zhang F, Luk T. A data mining algorithm for monitoring PCB assembly quality. IEEE Transactions on Electronics Packaging Manufacturing 2007;30(4):299–305. [15] Zadeh LA. Fuzzy sets. Information and Control 1965;8:338–53. [16] Ross PJ. Taguchi techniques for quality engineering. New York: McGraw-Hill; 1996. [17] Hedayat AS, Sloane NJA, Stufken J. Orthogonal arrays: theory and applications. New York: Springer-Verlag; 1999. [18] Benardos PG, Mosialos S, Vosniakos G-C. Prediction of workpiece elastic deflections under cutting forces in turning. Robotics and Computer-Integrated Manufacturing 2006;22:505–14. [19] Gaitonde VN, Karnik SR, Achyutha BT, Siddeswarappa B. Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size. International Journal of Advanced Manufacturing Technology 2007;34:1–8. [20] Phadke MS. Quality engineering using robust design. AT&T Bell Labs; 1989. [21] Taguchi G. System of experiment design. New York: Unipub/Kraus International; 1987. [22] Tzeng Y-F, Chen FC. Multi-objective optimization of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Materials and Design 2007;28:1159–68. [23] Chen KS, Wu CH, Chen SC. Criteria of determining the P/T upper limits of GR&R in MSA. Quality and Quantity 2008;42:23–33. [24] Rout BK, Mittal RK. Screening of factors influencing the performance of manipulator using combined array design of experiment approach. Robotics and Computer-Integrated Manufacturing 2009;25:651–66. [25] Saaty TL. Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburgh: RWS Publications; 2000. [26] Saaty TL. The analytic hierarchy process: planning, priority setting and resource allocation. NY: McGraw-Hill; 1980. [27] Sipahi S, Timor M. The analytic hierarchy process and analytic network process: an overview of applications. Management Decision 2010;48(5):775–808. [28] De Feo JA, Barnard W. JURAN institute’s six sigma breakthrough and beyond—quality performance breakthrough methods. New York: Tata McGraw-Hill Publishing Company Limited; 2005. [29] Pedrycz W. Why triangular membership functions? Fuzzy Sets and Systems 1994;64:21–30. [30] Zimmermann H-J. Fuzzy set theory and its applications. Massachusetts: Kluwer; 1996. [31] Lathrop RR. Solder paste print qualification using laser triangulation. IEEE Transactions on Electronics Packaging and Manufacturing 1997;20:174–82.