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Study of tip wear for AFM-based vibration-assisted nanomachining process
Journal of Manufacturing Processes 50 (2020) 47–56
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Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Study of tip wear for AFM-based vibration-assisted nanomachining process
T
Xiangcheng Kong, Jia Deng, Jingyan Dong*, Paul H. Cohen Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Tip-based nanomachining Atomic force microscope (AFM) Tip wear monitoring Tip wear modeling
Nanofabrication technologies have many applications in science and engineering. Among different nanofabrication technologies, the tip-based vibration-assisted nanomachining using an Atomic Force Microscope (AFM) provides a low-cost, easy-to-setup approach for the production of nano-scale structures. As the resolution and quality of the machined features are greatly affected by the radius and sharpness of the tip, it is critical to investigate the behavior of tip wear during the nanomachining process and to estimate the tip life. In this work, the evolvement of the tip wear was characterized and modeled to predict tip wear and tip life for the nanomachining process. Besides the direct inspection of the tip radius using a Scanning Electron Microscope (SEM), the pull-off force between the AFM tip and the sample surface was found to correlate well with the tip radius, which enabled the measurement of tip wear directly without unloading the tip from the AFM. To study the tip wear at different conditions, the tip radius was measured from the pull-off force under a wide range of machining conditions. The change rates of the tip radius were significantly affected by the machining parameters, such as setpoint force and feed rate. Moreover, during the nanomachining process, three regions were identified for the tip wear evolvement as initial tip wear region, transition region, and tip failure region. Regression models were developed to describe the tip wear at different stages, and to estimate the tip life (i.e. when the tip needs to be changed), which provide usefully information for future process planning and process optimization.
1. Introduction Nanofabrication, especially the fabrication of master patterns and masks, is critical for emerging nanotechnology applications including fundamental physics, chemistry, electronics, materials, and biology. There are many nanofabrication methods that were able to fabricate nanopatterns and nanostructures, including X-ray lithography [1–3], EUV lithography [4,5], e-beam lithography [6,7], nanoimprint lithography [8,9], Dip-pen nanolithography [10] etc. Despite impressive resolution from these methods, many of them rely on the masks and molds fabricated by e-beam lithography to enable their processes. The e-beam lithography systems are very expensive to acquire and to maintain. Compared with the expensive e-beam lithography system with very high hourly rate, tip-based nanofabrication with Atomic Force Microscope (AFM) uses lower-cost equipment with easy-to-setup system and tool to produce nanoscale features. The hourly rate for using an AFM is much lower than that for using an e-beam system, although the short tip life could increase the cost of AFM based nanofabrication. Other than the cost, the AFM based nanofabrication provides comparable resolution to that from the e-beam lithography. Tip-based nanomachining using an Atomic Force Microscope (AFM)
is a low-cost approach for the production of nano-scale structures. The simplest application of tip-based nanomachining is direct scratching using a sharp AFM tip, in which the sample surface is modified mechanically by cutting or plastic deformation. Direct scratching is easy to apply, but generally has low manufacturing throughput. Since the size of the produced feature is primarily affected by the tip radius, multiple scratching steps have to be used to machine large features. Furthermore, in the direct scratching process, a large normal force is a prerequisite to ident the tip into the sample for mechanical machining [11–13], which causes severe tip wear. To overcome these disadvantages, researchers have developed and demonstrated vibrationassisted nanomachining approaches for low-cost high-rate nanofabrication [14–16]. Three-dimensional nanostructures were successfully fabricated using this nanomachining process [17,18]. With the assistance of vibration, the machining force as well as tip wear were significantly reduced. To understand the mechanism of the tip-based vibration-assisted nanomachining process, the tip-sample interaction forces (i.e. the machining forces) involved in this process and the resultant feature dimensions under given machining parameters have been analyzed and modeled in previous research [19,20], which provided useful guidelines for selecting the machining parameters and
⁎ Corresponding author at: Department of Industrial and Systems Engineering, North Carolina State University, 414-C Daniels Hall, Campus box 7906, Raleigh, North Carolina, 27695-7906, USA. E-mail address: [email protected] (J. Dong).
https://doi.org/10.1016/j.jmapro.2019.12.013 Received 25 June 2019; Received in revised form 6 December 2019; Accepted 9 December 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. (a) Schematics of the vibration assisted nanomachining system. (b) Tip vibration in xy-plane control the feature width in one machining pass. (c) The top view of machining process shows the tip-sample engagement during one rotation cycle.
these studies, the tip wear were mostly observed by a scanning electron microscope (SEM) or transmission electron microscopy (TEM) by measuring the tip radius and the evolvement of tip profile [37[36]]. Although SEM and TEM provide very accurate measurement of the tip wear and tip radius, the machining operation has to be interrupted frequently to take off the tip from the AFM for the off-line SEM or TEM inspection and measurement. To overcome such inconvenience and to monitor the tip radius continuously, the pull-off forces between the AFM tip and the sample surface was measured and correlated with the AFM tip radius [37] to study the tip wear under simple sliding between the tip and the sample. In this paper, the tip wear behavior for the vibration assisted nanomachining process was systematically studied with process models being developed to understand the correlation between process conditions and corresponding tip life. In addition to the SEM inspection, the pull-off force between the AFM tip and the sample surface was characterized and ultilized to measure the tip wear directly without unloading the tip from the AFM and interupting the machining process. To study the tip wear at different conditions, the tip radius was measured from the pull-off force under a wide range of machining conditions. During the nanomachining process, three regions (i.e. initial tip wear region, transition region, and tip failure region) were observed as the tip was gradully worn off. Regression models were developed to describe the tip wear at different stages, and to estimate the tip life (i.e. when the tip needs to be changed), which provide usefully information for future proces planning and process optimization.
planning the process properly to improve productivity. In these models, the tip radius was assumed constant during the machining process, which is reasonable for brand new tips that have only undergone light machining tasks. However, when a tip was used in nanomachining for high volume production, the tip can be severely worn resulting in a much larger tip radius than that of a new tip. Such tip wear can significantly affect the machining performance and feature resolution. It is critical to understand the tip wear and estimate the tip life for the nanomachining process. For the tool wear at the conventional scale, many monitoring and modeling methods have been developed to predict its behavior, including analytical modeling, finite element analysis, and empirical modeling techniques. These modeling methods have been applied to different machining processes like turning, milling, and other cutting processes [21–25]. Analytical models have been developed to describe the evolution of tool wear during different machining processes [26–28]. The cutting conditions and average tool flank wear during milling process were correlated in the model, in which the cutting power was employed to calculate the normal cutting power for tool wear monitoring. Due to the difficulties to build analytical models, numerical methods have also been adapted to predict the tool wear during machining processes. A thermo-mechanical discrete element model has been developed to study the cutting process [29], which considered both mechanical and thermal phenomena and their reciprocal influence for the cutting process. Finite element method (FEM) has also been used to investigate and optimize the tool wear in drilling process for the difficult-to-cut nickel-based superalloy [30]. The effect of the process variables including cutting speed, feedrate and tool diameter on tool wear were analyzed and characterized. A tool wear model for twist drill was then built to study the mechanism of drill wear. The result from the FEA model was demonstrated to match well with the experimental data. For AFM imaging and tip-based fabrication processes, the tip radius is a critical factor that determines the image quality or dimensional accuracy of the machined features. However, the tip wear is an inevitable result of both the imaging and tip-based nanomachining processes. The consequences of tip wear include the degradation of the image resolution, false measurement, reduced fabrication resolution, and reduced fabrication capability. Due to the significant differences in the tools and machining mechanisms between the conventional scale machining and micro/nanometer scale tip-based machining, many of the tool wear models for the conventional machining process cannot be directly applied for micro/nano scale machining processes. A few atomic-level modeling methods [[31–35]] have been developed to study the micro/nano-scale machining process, such as molecular dynamics method (MD), Monte Carlo (MC) simulation method. Some initial parametric and exprimental studies were performed to observe and understand the tip wear in nanomachining applications [31,32]. In
2. Experimental setup for vibration-assisted nanomachining process The nanomachining experiments were performed on a commercial AFM, Park XE-70 (Park Systems Corp®) with a customized nano-vibration system (Fig. 1(a)) [14]. The lab customized nano-vibration system provided the tip-sample vibration to implement the vibrationassisted nanomachining process. To monitor the machining process, the signals from four-quadrant photodetector in the AFM were used to measure the normal deflection and torsion of the cantilever as A–B and C–D signals. The normal deflection and torsion of the cantilever, representing the normal force and lateral forces respectively during the nanomachining process, were acquired by LabView through a data acquisition device (NI USB-6295). The command signals (i.e. synchronized sinusoid signals with 90° phase difference) were also generated by the data acquisition device to drive the xy-piezo actuators for xy inplane circular vibration. We used a tapping mode cantilever with a nominal stiffness of 48 N/ m and a resonant frequency of 190 kHz as the tool for the nanomachining process. A PMMA film was used as the sample to perform tip-based vibration-assisted nanomachining. The PMMA film was spin48
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coated on a silicon substrate with about 40 nm in thickness after baking to dry. The sample was mounted on the nano-vibration system to be vibrated in the xy plane at a frequency of 2 KHz. To simplify the modeling process, vertical z-vibration was not applied in this study. With the xy-vibration, the width of the machined trench can be easily controlled in one machining pass, as shown in Fig. 1(b). Moreover, with the assistance of the xy-vibration, the material removal was distributed to each rotational cycle, only a small slice of material was removed in each cycle (Fig. 1(c)), which effectively reduced the resulting machining force and increased the overall machining speed [14,15].
which high-quality structures were reliably produced. When the tip was used for certain machining operations and gradually worn, a transition stage was observed. At this stage, machining results became unreliable and the trench depth reduced rapidly to below 5 nm (Fig.3(b)) due to the larger tip radius. Finally, at the tip failure stage, the tip was severely worn and tip radius became very large. The worn tip cannot remove materials effectively (Fig. 3(c)). 3.2. In-situ tip wear detection Although the SEM images can be used to detect tip wear and measure tip-radius effectively, this process is time-consuming and cannot be performed in-situ. To take SEM images of the AFM tip, the tip has to be removed from the AFM, resulting in the interruption of the machining process and reduction in the productivity. To solve this problem, a lowcost, easy-to-implement method is needed to detect the tip radius and tip wear without interrupting the nanomachining process. A pull-off (or adhesive) force is seen when the AFM tip is retracted from the sample, which provides a promising method to estimate tip radius and to detect tip wear [38]. The pull-off force can be conveniently measured from the force-distance response curve when approaching the tip to the sample first and then retracting the tip from the sample. A typical force-distance curve is shown in Fig. 4. The vertical axis represents the normal force applied on the cantilever due to the interaction between the tip and the sample, while the horizontal axis is the displacement of the cantilever. When the tip is far away from the sample surface, the cantilever has no deflection and zero tip-sample interaction force. As the tip gradually approaches the sample, the cantilever begins to deflect toward the sample, due to an attractive force between them. After the tip physically touch and push the sample, the cantilever is deflected away from the surface due to the repulsive interaction force between the tip and the sample. Finally, when the cantilever is retracted from the sample, the tip-sample contact breaks at a value defined by the pull-off force, which is the adhesion force between the tip and the sample. This adhesion force is related to the tipsample contact and interaction. For a blunt tip with a large tip radius, there is a larger contact between the tip and the sample, causing a larger force to retract the tip. As a result, the pull-off force is a good indicator of the tip radius. In this study, the force-distance curve can be easily measured immediately following each machining operation. After machining on the PMMA film, the AFM tip was moved to an area of silicon to measure the force-distance curve using an “indentation mode.” Since the humidity level could significantly affect the measured pull-off force, the measurement experiments were conducted when the ambient humidity was between 16% and 21%. During the measurement, both the approach and retraction speed were set as 0.01 μm/s, and the applied normal force was set as 200 nN. After the tip pressed the sample at 200 nN, the cantilever was slowly retracted. The force-distance curves were obtained for the both approach and retraction stages, which were used to calculate the pull-off force. In Fig. 5(a), the measured pull-off force and the tip radius measured by the SEM were compared in the same plot. As can be observed, the changing trend of the pull-off forces matches very well with that of the tip radius, and the good correlation between these two variables can be clearly observed. Therefore, the pull-off force was used as an alternative for measuring the tip radius, which can be used to monitor and study the tip wear without interrupting the machining process. The pull-off forces measured at different moments provided detailed information about the evolvement of the tip wear during the nanomachining process. Fig. 5(b) showed the development of tip wear and tip radius (reflected by the pull-off force) for a typical nanomachining condition with the setpoint force and feed rate at 1000 nN and 2 μm/s respectively. As the tip worn from the machining operation, the tip radius became larger, and a larger pull-off force was measured during the tip retraction process.
3. Detection of tip wear during nanomachining process 3.1. Tip wear measurement and impact on features produced During the nanomachining process, tool wear is generated by the direct contact and sliding motion between the cutting tool (AFM tip) and the workpiece. Along with the development of tip wear, the machining force, as well as the cutting temperature, will increase, which will degrade the dimensional accuracy and surface quality of the produced features. Moreover, the tip has to be replaced when the machining performance become unsatisfactory. The change of the tools in the nanomachining process increases the machining cost and reduces the productivity. As a result, tool wear is a critical factor that greatly affects the quality and productivity of the nanomachining process. For the tip-based vibration-assisted nanomachining, the effect of tool wear is even more significant as the dimensions of the machined patterns are affected by the dimensions of the AFM tip. The radius of the AFM tip is usually around tens of nm, while the depth of machined patterns is at the same range. As a result, when the radius of an AFM tip increased by a small amount, the dimensions of machined patterns are greatly influenced. Unlike conventional scale machining, in which many models have been studied to analyze the tool wear during the machining process, little research has been performed for the tip wear in the nanometer scale machining process. To study the tip wear systematically during the nanomachining process, parallel experiments were conducted. Six new tips were applied to machine 0, 2, 4, 8, 12 and 20 identical patterns. Each designed pattern includes 32 identical trenches with a length of 5 μm and a width of 60 nm that were machined using the same machining parameters (with the setpoint force, feed rate, and XY vibration amplitude to be 1000 nN, 2 μm/s and 40 mV respectively) to study the evolution of tip wear and the resulting machining performance. Each tip was observed in the SEM after the machining of the requisite number of patterns. Fig. 2(a) provides the SEM images of six tips at different wear stages. With the ThreePointCircularROI, a plugin of Image J software, a circle was created based on three selected points on the cutting edge of the tip, whose radius was used to estimate the corresponding tip radius. With this method, the radius of these tips were measured, and the change in tip radius during the machining process was recorded, as shown in Fig. 2(b). Clearly, after a tip was used to produce more patterns, the tip experienced more wear, resulting a larger tip radius. When using a brand-new tip with sharp tip radius, the trenches produced had large and uniform depths ranging from 20 nm to 25 nm (Fig.3(a)). During machining, the tip gradually wore yielding a larger tip radius. The blunt tip with larger radius also reduced the trench depth. When the tip was severely worn and tip radius was too large, its machining capability to produce nanoscale features was significantly degraded, because with a large tip radius, the given setpoint force cannot produce enough stress for the tip to indent into the sample. For the recorded depth in Fig. 3(d), the depth of each trench was measured at five different locations. The variations from these measurements were pretty small (less than 1−2 nm), which is close to the measurement resolution of the AFM system. From our observation of experimental results, the evolvement of the tip wear can be categorized into three stages. When a new tip was used, the tip was sharp enough from 49
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Fig. 2. (a) SEM images for tip 1–6 after machining different number of groove patterns. (b) Measured tip radius at different total machining distance (one pattern is 160 μm in machining distance).
4. Tip wear behaviors under different machining conditions
tip radius, the depth of the machined features decreased. After certain numbers of the patterns were machined for each machining condition, the depth of the machined features decreased rapidly, indicating severe tip wear and large tip radius that became ineffective to remove material at the given machining condition. Moreover, from the evolution of the pull-off force in Figs. 6–8, two regions (at the beginning of the machining experiments vs. at the end of machining experiments) can be clearly observed, which have quite different wear rates (i.e. change rate of the tip radius or pull-off force). These two regions corresponded to the two different stages of the nanomachining, as the initial tip wear region and the tip failure region as we discussed before. For the first stage (i.e. initial tip wear region), the tip was still sharp enough to remove material, while at the late stage (i.e. the tip failure region), the tip became very blunt and cannot effectively produce trenches on the sample. The tip wear behavior with and without effective machining was quite different. During effective machining when the tip can be used to remove materials and produce trenches with large depth, the change rate of the pull-off force (i.e. tip radius) was smaller compared with the change rate when the tip cannot effectively remove material and produce trenches on the sample. For the later condition, the tip simply slid on the sample surface, resulting in a faster wear rate. In between these two regions, a transition region was defined to describe the change between these two stages, in which the depth of the machined features decreased rapidly, and the machining process became unreliable. The process conditions have strong effects on the tip wear behavior, as shown in Figs. 6–8. For these different machining conditions, the change rates of the pull-off forces can be estimated when the tips were capable to machine the patterns on the sample. Clearly, the change
The tip wear behaviors were different under different process conditions, such as setpoint force and feedrate in nanomachining. It is critical to understand the effects of these machining parameters on the tip wear and develop the tip wear models to estimate the tip life and the machined distance before the tip has to be changed. To understand the impact of the machining parameters (i.e. setpoint force and feed rate) on the tip wear, a full factorial experiment with these two factors was designed and conducted. For each factor, three different values were assigned corresponding to the low, medium, and high level of this factor, as shown in Table 1. To implement this factorial design experiment, nine new tips were applied to machine trenches with different machining parameters, as shown in Table 1. For each machining condition, the tip was used to machine multiple patterns until it was fully worn out, and cannot produce noticeable features on the sample. The machined patterns were imaged with their feature depth measured using the same AFM at the tapping mode. After finish machining each pattern with 32 trenches, the pull-off forces were measured using the “indentation mode” to reflect the tip wear at that moment. The values of measured pull-off force and average feature depth during the nanomachining process at three different setpoint forces (500 nN, 1000 nN and 1500 nN respectively) and three feedrate (1 μm/ s, 2 μm/3, and 3 μm/s) were plotted from Figs. 6–8. Clearly, from these figures, similar trends can be observed for the change in the pull-off force (i.e. tip radius). For each machining condition, the pull-off forces increased when more patterns were machined using that tip, indicating the increased tip radius. In the meantime, along with the increase of the
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Fig. 3. (a) The pattern machined with a brand-new tip during initial tip wear region. (b) The pattern machined with a partially worn tip during transition region. (c) The pattern machined with a severely worn tip during tip failure regions. (d) The measured feature depth during machining process from different tips, represented by different colors.
Fig. 4. Force-distance curve obtained from an AFM when approaching the tip to and retracting the tip from the sample. The pull-off force, as the maximum attraction force during retraction, is proportional to the tip radius.
rates of the pull-off forces (that is the rate of the tip wear) during nanomachining increased with a larger setpoint force and a larger feed rate. This behavior is easy to understand, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Fig. 5. (a) Plot of the measured tip radius using SEM and the pull-off force. The pull-off force is strong correlated with the tip radius. (b) A typical evolvement of the measured pull-off forces during the nanomachining process under 1000 nN setpoint force and 2 μm/s feed rate. In the plots, one pattern is 160 μm in machining distance.
5. Modeling of the tip wear and tip life To successfully apply and plan the tip-based nanomachining process, it is critical to understand tip wear at different machining conditions and estimate the life of an AFM tip, so as to decide when to change the tip to ensure the machining performance and plan the 51
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the tip is relatively sharp and can effectively remove materials to machine trenches on the sample with large depth. Both the pull-off forces and the change rates of the pull-off forces are small and the quality of machined patterns in this region is good. After the tip finished machining a number of patterns, the transition region is observed. In the transition region, the pull-off force (i.e. an indication of the tip radius) increases rapidly, indicating the quick wear of the tip. In the meantime, the depth of the machined features decreases rapidly. In the final tip failure region, due to the large tip radius, with the given machining condition, features/trenches cannot be effectively machined on the sample. The tip is simply sliding on the surface of the sample without removing any material. The change rate of the pull-off force is much larger than that in the initial tip wear region, which indicates a fast wear rate of the tip. Fig. 9 provides a typical example of different tip wear regions during the machining process. For the machining of the first nine patterns, the machined feature depth decreases slowly, and the pull-off force also increases slightly. Between the machining of the 10th pattern and the 13th pattern, the depth of machined features rapidly decreases. Finally, after the 13th pattern, no noticeable trenches can be machined with feature depth close to zero while the pull-off force continuously increases with a steeper slope than before. Since each pattern machined in the experiments included 32 lines with the length of each line of 5 μm, it is more formal to describe change of pull-off force with respect to the total machined linear distance (160 μm for each pattern machined), regardless of whether the machined trench is visible or not. The pull-off forces during the initial tip wear region and the tip failure region for all the machining conditions (Figs. 6–8) were analyzed. Fig. 10 is an example for one
Table 1 Factorial design experiments for tip wear study. Test number
Setpoint force/nN
Feed rate/μm/s
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
nanomachining process efficiently with the minimized cost. As can be seen in Figs. 6–8, the change rates of the pull-off force and the machined feature depth were different under different machining conditions. To model the effect of different process conditions on the tip wear rate, we need to study and quantify the change rate of the pull-off force and compare the results among different machining conditions. In Fig. 9, to ease the observation of the evolution of the tip wear and the resulting feature depth at each wear stage, the pull-off force and the corresponding feature depth (x10) for one machining condition (i.e. 1000 nN setpoint force and 2 μm/s feedrate) were plotted together. Based on the change rate of the pull-off force and the machined feature depths, the nanomachining process can be roughly divided into three regions: Initial tip wear region, Transition region, and Tip failure region. The initial tip wear region starts when a new tip begins to be used for nanomachining and produces the first few patterns. In this region,
Fig. 6. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 500 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance. 52
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Fig. 7. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 1000 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance.
machining condition as given in Fig. 9. The regression models fit the data very well with goodness of fit R-squared at about 0.95. The X axis is the machined linear distance (μm), while the Y axis is the pull-off force (nN) measured after each machined pattern. For both the initial tip wear region and tip failure region, linear regression lines were fitted to correlate the pull-off forces with the machined linear distance under each machining condition, which were defined as the local fitting lines. The slopes of these local fitting lines represent the change rate of the pull-off forces with respect to the machined distance. For the initial tip wear region, the intercepts of the line with the vertical axis represent the extrapolated pull-off force for a brand-new tip before machining. Table 2 lists the change rates of the pull-off forces for the initial tip wear region and tip failure region under all nine different machining conditions. Clearly, the change rates of the pull-off forces (that reflects the wear rate of the tip) increased with a larger setpoint force and a larger feed rate. This behavior is easy to understand, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Moreover, for each machining condition, the tip wear rate is much higher (more than three times) at the tip failure region than that from the initial tip wear region. The difference in the wear rate for these two regions can be explained intuitively. At the initial tip wear region, the tip is sharp enough to indent into the sample’s surface for mechanical machining. Both the bottom of the tip and the side of the tip interact with the sample. Tip wear happens at the bottom surface and the side surface, although the wear rate at the bottom tends to be larger. The tip wear on the side surface sharpens the tip and effectively reduces the increase rate of the tip radius. At the tip failure region, the tip is too blunt to be indented into the sample to have effective machining. The tip simply slides on the sample, and the resulting tip wear mainly
happens at the bottom surface, which rapidly increases the tip radius, as indicated by the large change rate of the pull-off force. To study the effect of the machining conditions (i.e. setpoint force and feedrate) on the tip wear, process models were developed to describe the progression of the tip wear and estimate the transition between the initial region and the tip failure region. The transition from the initial region to the tip failure region is an indicator of tip life. From Figs. 6–8, approximately linear trends are observed for the tip wear with respect to the machined distance for both the initial region and the tip failure region. Linear regression models were developed to correlate the pull-off forces with the machined linear distance during both the initial tip wear region and tool failure region. In the linear regression model, both the slope and intercept were correlated with machining parameters that were setpoint force and feedrate, which was shown as Eq. 1.
Fp = slope × Dlinear +intercept k2 F slope = k1 ⎛ set ⎞ feed k3 + k 4 ⎝ 1000 ⎠ k6 F intercept= k5 ⎛ set ⎞ feed k7 + k8 ⎝ 1000 ⎠
(1)
Fp is the pull-off forces measured during the initial tip wear region (nN), Dlinear (μm) is the machined linear distance, Feed is the feed rate (μm/s), Fset is the applied setpoint force (nN). In the model, the setpoint force is scaled (by a factor of 1000) to make the value of the force and feedrate of comparable magnitude. slope and intercept represent the slope and intercept of the linear regression model where k1, k2, k3, k 4 and k5 are regression coefficients. With the pull-off forces and corresponding 53
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Fig. 8. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 1500 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance.
Fig. 9. The measured pull-off forces and the feature depth (x10) as a function of the number of machined patterns. The blue dots represent the machined feature depth, while the boxes represent the pull-off forces. Based on the rate of change of pull-off forces or feature depth, the tip wear process can be identified as three regions: Initial tip wear region, transition region and tip failure region. In the plot, one pattern is 160 μm in machining distance. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 10. The plot of both global fitting line and local fitting line for (a) initial tip wear region and (b) tip failure region when the setpoint force and feed rate were set as 1000 nN and 2 μm/s. The blue dots represent the measured pull-off forces, the red lines represent the local fitting lines and the green lines represent the global fitting lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
machining parameters applied, this regression model was calibrated and all the coefficients were determined, as shown in Eq. 2.
Finitial = slope *Dlinear +intercept 2.76 F slope=0.019*⎛ set ⎞ *feed 0.23 + 0.0083 ⎝ 1000 ⎠ intercept = 26.56
(2)
the tips used in the experiments before machining. Similarly, a regression model was developed and calibrated for the tip failure region, which is shown as Eq. 3.
As can be observed, the slope (or the wear rate) increases with the larger setpoint force and feed rate, and the intercept is calculated as 26.56 nN, which is close to the average of measured pull-off forces for 54
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indication of tip life for that specific machining condition. From the regression model, two lines were used to describe the evolvement of tip wear along with the machined distance. The intersection point of the two lines for both the initial tip wear region and tip failure region provided an estimation of the transition point, from which the change rate of the pull-off force increased rapidly. The specific machined linear distance at the intersection point can also be calculated from Eqs. 2 and 3 when the calculated Ffailure equal to Finitial at each machining condition. Fig. 11 provided an example showing the lines from regression model (with goodness of fit R-squared at 0.93) and the transition point indicating the tip life when the setpoint force and feed rate were 1000 nN and 2 μm/s respectively. Table 3 listed the estimated transition point in both the machined distance and the number of the machined patterns. The machined feature depth around the transition point was also listed in Table 3. Compared with the evolvement of the tip wear from Figs. 6–8, these transition points from the regression model provided a rough estimation about of tip life about when to change the tip to achieve acceptable machining performance.
Table 2 The wear rate measured by the change rate of the pull-off forces (nN/ μm) for the initial tip wear region and tip failure region under different machining conditions. Test #
Setpoint force (nN)
Feed rate (μm/s)
Wear rate at initial region (nN/μm)
Wear rate at failure region (nN/μm)
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
0.0097 0.0083 0.0185 0.0209 0.0289 0.0382 0.0691 0.0755 0.0819
0.0292 0.0426 0.0667 0.0623 0.1123 0.1379 0.1528 0.1971 0.2519
6. Summary In this work, the tip wear during the tip-based vibration-assisted nanomachining process was systematically studied, and process models were developed to understand the correlation between process conditions and corresponding tip life. In addition to the SEM inspection, the pull-off force between the AFM tip and the sample surface was applied to measure the tip wear directly without unloading the tip from the AFM and without interupting the machining process. To study the effect of the machining parameters, such as setpoint force and feed rate, on the tip wear process, a full factorial design experiment was performed to study the effects of these machining parameters on the tip wear, and to develop the tip wear models to estimate the tip life and the machined distance for the tip change. During the nanomachining process, three regions (i.e. initial tip wear region, transition region, and tip failure region) were observed as the tip was gradully worn off. The wear rate of the tip was higher with a larger setpoint force and a larger feed rate, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Moreover, the tip wear rate was much high at the tip failure region than that from the initial tip wear region. Regression models were developed to describe the tip wear behavior for the initial tip wear region and the tip failure region, which provided a methodology to estimate the tip life (i.e. when the tip needs to be changed). The model of the tip wear and tip life can potentially provide usefully guidelines for future proces planning and process optimization.
Fig. 11. Lines from regression model for the initial regions and tip failure region. The intersection of the two lines provide an estimation of the transition point where the tip need to be changed. Table 3 The estimated transition point from the regression model and the feature depth at the transition point. Test #
Setpoint force (nN)
Feed rate (μm/s)
Machined distance (μm)
Number of patterns machined
Machined feature depth (nm)
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
1328 1456 1504 2032 1952 1872 2624 2320 2208
8.3 9.1 9.4 12.7 12.2 11.7 16.4 14.5 13.8
5 2 1 6 2 1 4 4 5
Ffailure = slope *Dlinear +intercept 1.78 F slope=0.066*⎛ set ⎞ *feed 0.55 − 0.0089 ⎝ 1000 ⎠ 1.72 F intercept=-101.57*⎛ set ⎞ *feed 0.62 + 33.66 1000 ⎝ ⎠
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(3)
where Ffailure is the pull-off force measured during the tip failure region (nN). Similar to the regression model for the initial tip wear region, the slope (or wear rate) increases with the larger setpoint force or feed rate. In Fig. 10, the pull-off forces were plotted versus the machined linear distance when the setpoint force and feed set as 1000 nN and 2 μm/s separately. As can be observed, for both the initial tip wear region and tip failure region, the lines from the regression models fits well to the changing trend of the measured pull-off force. Using the regression model, the life of the tip can be roughly estimated as well. For the tip-based nanomachining process, the patterns machined during the initial tip wear region are usually acceptable, while during the tip failure region, the tip rapidly wears out, and no trenches can be reliably machined. To provide the guideline for the tip change, we need to identify a machined distance where pattern quality rapidly degrades, which is termed as the “transition point” and is an
Acknowledgement This work was supported in part by the National Science Foundation under Grant Award NSF CMMI-1233176. References [1] Smith HI, Schattenburg ML. X-ray lithography from 500 to 30 nm: X-ray nanolithography. IBM J Res Dev 1993;37(3):319–29. [2] Silverman JP. X-ray lithography: status, challenges, and outlook for 0.13 μm. J Vac Sci Technol B Microelectron Nanometer Struct Process Meas Phenom 1997;15(6):2117–24. [3] Griffiths SK. Fundamental limitations of LIGA x-ray lithography: sidewall offset, slope and minimum feature size. J Micromechanics Microengineering 2004;14(7):999. [4] Wagner C, Harned N. EUV lithography: Lithography gets extreme. Nat Photonics 2010;4(1):24.
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Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Study of tip wear for AFM-based vibration-assisted nanomachining process
T
Xiangcheng Kong, Jia Deng, Jingyan Dong*, Paul H. Cohen Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Tip-based nanomachining Atomic force microscope (AFM) Tip wear monitoring Tip wear modeling
Nanofabrication technologies have many applications in science and engineering. Among different nanofabrication technologies, the tip-based vibration-assisted nanomachining using an Atomic Force Microscope (AFM) provides a low-cost, easy-to-setup approach for the production of nano-scale structures. As the resolution and quality of the machined features are greatly affected by the radius and sharpness of the tip, it is critical to investigate the behavior of tip wear during the nanomachining process and to estimate the tip life. In this work, the evolvement of the tip wear was characterized and modeled to predict tip wear and tip life for the nanomachining process. Besides the direct inspection of the tip radius using a Scanning Electron Microscope (SEM), the pull-off force between the AFM tip and the sample surface was found to correlate well with the tip radius, which enabled the measurement of tip wear directly without unloading the tip from the AFM. To study the tip wear at different conditions, the tip radius was measured from the pull-off force under a wide range of machining conditions. The change rates of the tip radius were significantly affected by the machining parameters, such as setpoint force and feed rate. Moreover, during the nanomachining process, three regions were identified for the tip wear evolvement as initial tip wear region, transition region, and tip failure region. Regression models were developed to describe the tip wear at different stages, and to estimate the tip life (i.e. when the tip needs to be changed), which provide usefully information for future process planning and process optimization.
1. Introduction Nanofabrication, especially the fabrication of master patterns and masks, is critical for emerging nanotechnology applications including fundamental physics, chemistry, electronics, materials, and biology. There are many nanofabrication methods that were able to fabricate nanopatterns and nanostructures, including X-ray lithography [1–3], EUV lithography [4,5], e-beam lithography [6,7], nanoimprint lithography [8,9], Dip-pen nanolithography [10] etc. Despite impressive resolution from these methods, many of them rely on the masks and molds fabricated by e-beam lithography to enable their processes. The e-beam lithography systems are very expensive to acquire and to maintain. Compared with the expensive e-beam lithography system with very high hourly rate, tip-based nanofabrication with Atomic Force Microscope (AFM) uses lower-cost equipment with easy-to-setup system and tool to produce nanoscale features. The hourly rate for using an AFM is much lower than that for using an e-beam system, although the short tip life could increase the cost of AFM based nanofabrication. Other than the cost, the AFM based nanofabrication provides comparable resolution to that from the e-beam lithography. Tip-based nanomachining using an Atomic Force Microscope (AFM)
is a low-cost approach for the production of nano-scale structures. The simplest application of tip-based nanomachining is direct scratching using a sharp AFM tip, in which the sample surface is modified mechanically by cutting or plastic deformation. Direct scratching is easy to apply, but generally has low manufacturing throughput. Since the size of the produced feature is primarily affected by the tip radius, multiple scratching steps have to be used to machine large features. Furthermore, in the direct scratching process, a large normal force is a prerequisite to ident the tip into the sample for mechanical machining [11–13], which causes severe tip wear. To overcome these disadvantages, researchers have developed and demonstrated vibrationassisted nanomachining approaches for low-cost high-rate nanofabrication [14–16]. Three-dimensional nanostructures were successfully fabricated using this nanomachining process [17,18]. With the assistance of vibration, the machining force as well as tip wear were significantly reduced. To understand the mechanism of the tip-based vibration-assisted nanomachining process, the tip-sample interaction forces (i.e. the machining forces) involved in this process and the resultant feature dimensions under given machining parameters have been analyzed and modeled in previous research [19,20], which provided useful guidelines for selecting the machining parameters and
⁎ Corresponding author at: Department of Industrial and Systems Engineering, North Carolina State University, 414-C Daniels Hall, Campus box 7906, Raleigh, North Carolina, 27695-7906, USA. E-mail address: [email protected] (J. Dong).
https://doi.org/10.1016/j.jmapro.2019.12.013 Received 25 June 2019; Received in revised form 6 December 2019; Accepted 9 December 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. (a) Schematics of the vibration assisted nanomachining system. (b) Tip vibration in xy-plane control the feature width in one machining pass. (c) The top view of machining process shows the tip-sample engagement during one rotation cycle.
these studies, the tip wear were mostly observed by a scanning electron microscope (SEM) or transmission electron microscopy (TEM) by measuring the tip radius and the evolvement of tip profile [37[36]]. Although SEM and TEM provide very accurate measurement of the tip wear and tip radius, the machining operation has to be interrupted frequently to take off the tip from the AFM for the off-line SEM or TEM inspection and measurement. To overcome such inconvenience and to monitor the tip radius continuously, the pull-off forces between the AFM tip and the sample surface was measured and correlated with the AFM tip radius [37] to study the tip wear under simple sliding between the tip and the sample. In this paper, the tip wear behavior for the vibration assisted nanomachining process was systematically studied with process models being developed to understand the correlation between process conditions and corresponding tip life. In addition to the SEM inspection, the pull-off force between the AFM tip and the sample surface was characterized and ultilized to measure the tip wear directly without unloading the tip from the AFM and interupting the machining process. To study the tip wear at different conditions, the tip radius was measured from the pull-off force under a wide range of machining conditions. During the nanomachining process, three regions (i.e. initial tip wear region, transition region, and tip failure region) were observed as the tip was gradully worn off. Regression models were developed to describe the tip wear at different stages, and to estimate the tip life (i.e. when the tip needs to be changed), which provide usefully information for future proces planning and process optimization.
planning the process properly to improve productivity. In these models, the tip radius was assumed constant during the machining process, which is reasonable for brand new tips that have only undergone light machining tasks. However, when a tip was used in nanomachining for high volume production, the tip can be severely worn resulting in a much larger tip radius than that of a new tip. Such tip wear can significantly affect the machining performance and feature resolution. It is critical to understand the tip wear and estimate the tip life for the nanomachining process. For the tool wear at the conventional scale, many monitoring and modeling methods have been developed to predict its behavior, including analytical modeling, finite element analysis, and empirical modeling techniques. These modeling methods have been applied to different machining processes like turning, milling, and other cutting processes [21–25]. Analytical models have been developed to describe the evolution of tool wear during different machining processes [26–28]. The cutting conditions and average tool flank wear during milling process were correlated in the model, in which the cutting power was employed to calculate the normal cutting power for tool wear monitoring. Due to the difficulties to build analytical models, numerical methods have also been adapted to predict the tool wear during machining processes. A thermo-mechanical discrete element model has been developed to study the cutting process [29], which considered both mechanical and thermal phenomena and their reciprocal influence for the cutting process. Finite element method (FEM) has also been used to investigate and optimize the tool wear in drilling process for the difficult-to-cut nickel-based superalloy [30]. The effect of the process variables including cutting speed, feedrate and tool diameter on tool wear were analyzed and characterized. A tool wear model for twist drill was then built to study the mechanism of drill wear. The result from the FEA model was demonstrated to match well with the experimental data. For AFM imaging and tip-based fabrication processes, the tip radius is a critical factor that determines the image quality or dimensional accuracy of the machined features. However, the tip wear is an inevitable result of both the imaging and tip-based nanomachining processes. The consequences of tip wear include the degradation of the image resolution, false measurement, reduced fabrication resolution, and reduced fabrication capability. Due to the significant differences in the tools and machining mechanisms between the conventional scale machining and micro/nanometer scale tip-based machining, many of the tool wear models for the conventional machining process cannot be directly applied for micro/nano scale machining processes. A few atomic-level modeling methods [[31–35]] have been developed to study the micro/nano-scale machining process, such as molecular dynamics method (MD), Monte Carlo (MC) simulation method. Some initial parametric and exprimental studies were performed to observe and understand the tip wear in nanomachining applications [31,32]. In
2. Experimental setup for vibration-assisted nanomachining process The nanomachining experiments were performed on a commercial AFM, Park XE-70 (Park Systems Corp®) with a customized nano-vibration system (Fig. 1(a)) [14]. The lab customized nano-vibration system provided the tip-sample vibration to implement the vibrationassisted nanomachining process. To monitor the machining process, the signals from four-quadrant photodetector in the AFM were used to measure the normal deflection and torsion of the cantilever as A–B and C–D signals. The normal deflection and torsion of the cantilever, representing the normal force and lateral forces respectively during the nanomachining process, were acquired by LabView through a data acquisition device (NI USB-6295). The command signals (i.e. synchronized sinusoid signals with 90° phase difference) were also generated by the data acquisition device to drive the xy-piezo actuators for xy inplane circular vibration. We used a tapping mode cantilever with a nominal stiffness of 48 N/ m and a resonant frequency of 190 kHz as the tool for the nanomachining process. A PMMA film was used as the sample to perform tip-based vibration-assisted nanomachining. The PMMA film was spin48
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coated on a silicon substrate with about 40 nm in thickness after baking to dry. The sample was mounted on the nano-vibration system to be vibrated in the xy plane at a frequency of 2 KHz. To simplify the modeling process, vertical z-vibration was not applied in this study. With the xy-vibration, the width of the machined trench can be easily controlled in one machining pass, as shown in Fig. 1(b). Moreover, with the assistance of the xy-vibration, the material removal was distributed to each rotational cycle, only a small slice of material was removed in each cycle (Fig. 1(c)), which effectively reduced the resulting machining force and increased the overall machining speed [14,15].
which high-quality structures were reliably produced. When the tip was used for certain machining operations and gradually worn, a transition stage was observed. At this stage, machining results became unreliable and the trench depth reduced rapidly to below 5 nm (Fig.3(b)) due to the larger tip radius. Finally, at the tip failure stage, the tip was severely worn and tip radius became very large. The worn tip cannot remove materials effectively (Fig. 3(c)). 3.2. In-situ tip wear detection Although the SEM images can be used to detect tip wear and measure tip-radius effectively, this process is time-consuming and cannot be performed in-situ. To take SEM images of the AFM tip, the tip has to be removed from the AFM, resulting in the interruption of the machining process and reduction in the productivity. To solve this problem, a lowcost, easy-to-implement method is needed to detect the tip radius and tip wear without interrupting the nanomachining process. A pull-off (or adhesive) force is seen when the AFM tip is retracted from the sample, which provides a promising method to estimate tip radius and to detect tip wear [38]. The pull-off force can be conveniently measured from the force-distance response curve when approaching the tip to the sample first and then retracting the tip from the sample. A typical force-distance curve is shown in Fig. 4. The vertical axis represents the normal force applied on the cantilever due to the interaction between the tip and the sample, while the horizontal axis is the displacement of the cantilever. When the tip is far away from the sample surface, the cantilever has no deflection and zero tip-sample interaction force. As the tip gradually approaches the sample, the cantilever begins to deflect toward the sample, due to an attractive force between them. After the tip physically touch and push the sample, the cantilever is deflected away from the surface due to the repulsive interaction force between the tip and the sample. Finally, when the cantilever is retracted from the sample, the tip-sample contact breaks at a value defined by the pull-off force, which is the adhesion force between the tip and the sample. This adhesion force is related to the tipsample contact and interaction. For a blunt tip with a large tip radius, there is a larger contact between the tip and the sample, causing a larger force to retract the tip. As a result, the pull-off force is a good indicator of the tip radius. In this study, the force-distance curve can be easily measured immediately following each machining operation. After machining on the PMMA film, the AFM tip was moved to an area of silicon to measure the force-distance curve using an “indentation mode.” Since the humidity level could significantly affect the measured pull-off force, the measurement experiments were conducted when the ambient humidity was between 16% and 21%. During the measurement, both the approach and retraction speed were set as 0.01 μm/s, and the applied normal force was set as 200 nN. After the tip pressed the sample at 200 nN, the cantilever was slowly retracted. The force-distance curves were obtained for the both approach and retraction stages, which were used to calculate the pull-off force. In Fig. 5(a), the measured pull-off force and the tip radius measured by the SEM were compared in the same plot. As can be observed, the changing trend of the pull-off forces matches very well with that of the tip radius, and the good correlation between these two variables can be clearly observed. Therefore, the pull-off force was used as an alternative for measuring the tip radius, which can be used to monitor and study the tip wear without interrupting the machining process. The pull-off forces measured at different moments provided detailed information about the evolvement of the tip wear during the nanomachining process. Fig. 5(b) showed the development of tip wear and tip radius (reflected by the pull-off force) for a typical nanomachining condition with the setpoint force and feed rate at 1000 nN and 2 μm/s respectively. As the tip worn from the machining operation, the tip radius became larger, and a larger pull-off force was measured during the tip retraction process.
3. Detection of tip wear during nanomachining process 3.1. Tip wear measurement and impact on features produced During the nanomachining process, tool wear is generated by the direct contact and sliding motion between the cutting tool (AFM tip) and the workpiece. Along with the development of tip wear, the machining force, as well as the cutting temperature, will increase, which will degrade the dimensional accuracy and surface quality of the produced features. Moreover, the tip has to be replaced when the machining performance become unsatisfactory. The change of the tools in the nanomachining process increases the machining cost and reduces the productivity. As a result, tool wear is a critical factor that greatly affects the quality and productivity of the nanomachining process. For the tip-based vibration-assisted nanomachining, the effect of tool wear is even more significant as the dimensions of the machined patterns are affected by the dimensions of the AFM tip. The radius of the AFM tip is usually around tens of nm, while the depth of machined patterns is at the same range. As a result, when the radius of an AFM tip increased by a small amount, the dimensions of machined patterns are greatly influenced. Unlike conventional scale machining, in which many models have been studied to analyze the tool wear during the machining process, little research has been performed for the tip wear in the nanometer scale machining process. To study the tip wear systematically during the nanomachining process, parallel experiments were conducted. Six new tips were applied to machine 0, 2, 4, 8, 12 and 20 identical patterns. Each designed pattern includes 32 identical trenches with a length of 5 μm and a width of 60 nm that were machined using the same machining parameters (with the setpoint force, feed rate, and XY vibration amplitude to be 1000 nN, 2 μm/s and 40 mV respectively) to study the evolution of tip wear and the resulting machining performance. Each tip was observed in the SEM after the machining of the requisite number of patterns. Fig. 2(a) provides the SEM images of six tips at different wear stages. With the ThreePointCircularROI, a plugin of Image J software, a circle was created based on three selected points on the cutting edge of the tip, whose radius was used to estimate the corresponding tip radius. With this method, the radius of these tips were measured, and the change in tip radius during the machining process was recorded, as shown in Fig. 2(b). Clearly, after a tip was used to produce more patterns, the tip experienced more wear, resulting a larger tip radius. When using a brand-new tip with sharp tip radius, the trenches produced had large and uniform depths ranging from 20 nm to 25 nm (Fig.3(a)). During machining, the tip gradually wore yielding a larger tip radius. The blunt tip with larger radius also reduced the trench depth. When the tip was severely worn and tip radius was too large, its machining capability to produce nanoscale features was significantly degraded, because with a large tip radius, the given setpoint force cannot produce enough stress for the tip to indent into the sample. For the recorded depth in Fig. 3(d), the depth of each trench was measured at five different locations. The variations from these measurements were pretty small (less than 1−2 nm), which is close to the measurement resolution of the AFM system. From our observation of experimental results, the evolvement of the tip wear can be categorized into three stages. When a new tip was used, the tip was sharp enough from 49
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Fig. 2. (a) SEM images for tip 1–6 after machining different number of groove patterns. (b) Measured tip radius at different total machining distance (one pattern is 160 μm in machining distance).
4. Tip wear behaviors under different machining conditions
tip radius, the depth of the machined features decreased. After certain numbers of the patterns were machined for each machining condition, the depth of the machined features decreased rapidly, indicating severe tip wear and large tip radius that became ineffective to remove material at the given machining condition. Moreover, from the evolution of the pull-off force in Figs. 6–8, two regions (at the beginning of the machining experiments vs. at the end of machining experiments) can be clearly observed, which have quite different wear rates (i.e. change rate of the tip radius or pull-off force). These two regions corresponded to the two different stages of the nanomachining, as the initial tip wear region and the tip failure region as we discussed before. For the first stage (i.e. initial tip wear region), the tip was still sharp enough to remove material, while at the late stage (i.e. the tip failure region), the tip became very blunt and cannot effectively produce trenches on the sample. The tip wear behavior with and without effective machining was quite different. During effective machining when the tip can be used to remove materials and produce trenches with large depth, the change rate of the pull-off force (i.e. tip radius) was smaller compared with the change rate when the tip cannot effectively remove material and produce trenches on the sample. For the later condition, the tip simply slid on the sample surface, resulting in a faster wear rate. In between these two regions, a transition region was defined to describe the change between these two stages, in which the depth of the machined features decreased rapidly, and the machining process became unreliable. The process conditions have strong effects on the tip wear behavior, as shown in Figs. 6–8. For these different machining conditions, the change rates of the pull-off forces can be estimated when the tips were capable to machine the patterns on the sample. Clearly, the change
The tip wear behaviors were different under different process conditions, such as setpoint force and feedrate in nanomachining. It is critical to understand the effects of these machining parameters on the tip wear and develop the tip wear models to estimate the tip life and the machined distance before the tip has to be changed. To understand the impact of the machining parameters (i.e. setpoint force and feed rate) on the tip wear, a full factorial experiment with these two factors was designed and conducted. For each factor, three different values were assigned corresponding to the low, medium, and high level of this factor, as shown in Table 1. To implement this factorial design experiment, nine new tips were applied to machine trenches with different machining parameters, as shown in Table 1. For each machining condition, the tip was used to machine multiple patterns until it was fully worn out, and cannot produce noticeable features on the sample. The machined patterns were imaged with their feature depth measured using the same AFM at the tapping mode. After finish machining each pattern with 32 trenches, the pull-off forces were measured using the “indentation mode” to reflect the tip wear at that moment. The values of measured pull-off force and average feature depth during the nanomachining process at three different setpoint forces (500 nN, 1000 nN and 1500 nN respectively) and three feedrate (1 μm/ s, 2 μm/3, and 3 μm/s) were plotted from Figs. 6–8. Clearly, from these figures, similar trends can be observed for the change in the pull-off force (i.e. tip radius). For each machining condition, the pull-off forces increased when more patterns were machined using that tip, indicating the increased tip radius. In the meantime, along with the increase of the
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Fig. 3. (a) The pattern machined with a brand-new tip during initial tip wear region. (b) The pattern machined with a partially worn tip during transition region. (c) The pattern machined with a severely worn tip during tip failure regions. (d) The measured feature depth during machining process from different tips, represented by different colors.
Fig. 4. Force-distance curve obtained from an AFM when approaching the tip to and retracting the tip from the sample. The pull-off force, as the maximum attraction force during retraction, is proportional to the tip radius.
rates of the pull-off forces (that is the rate of the tip wear) during nanomachining increased with a larger setpoint force and a larger feed rate. This behavior is easy to understand, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Fig. 5. (a) Plot of the measured tip radius using SEM and the pull-off force. The pull-off force is strong correlated with the tip radius. (b) A typical evolvement of the measured pull-off forces during the nanomachining process under 1000 nN setpoint force and 2 μm/s feed rate. In the plots, one pattern is 160 μm in machining distance.
5. Modeling of the tip wear and tip life To successfully apply and plan the tip-based nanomachining process, it is critical to understand tip wear at different machining conditions and estimate the life of an AFM tip, so as to decide when to change the tip to ensure the machining performance and plan the 51
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the tip is relatively sharp and can effectively remove materials to machine trenches on the sample with large depth. Both the pull-off forces and the change rates of the pull-off forces are small and the quality of machined patterns in this region is good. After the tip finished machining a number of patterns, the transition region is observed. In the transition region, the pull-off force (i.e. an indication of the tip radius) increases rapidly, indicating the quick wear of the tip. In the meantime, the depth of the machined features decreases rapidly. In the final tip failure region, due to the large tip radius, with the given machining condition, features/trenches cannot be effectively machined on the sample. The tip is simply sliding on the surface of the sample without removing any material. The change rate of the pull-off force is much larger than that in the initial tip wear region, which indicates a fast wear rate of the tip. Fig. 9 provides a typical example of different tip wear regions during the machining process. For the machining of the first nine patterns, the machined feature depth decreases slowly, and the pull-off force also increases slightly. Between the machining of the 10th pattern and the 13th pattern, the depth of machined features rapidly decreases. Finally, after the 13th pattern, no noticeable trenches can be machined with feature depth close to zero while the pull-off force continuously increases with a steeper slope than before. Since each pattern machined in the experiments included 32 lines with the length of each line of 5 μm, it is more formal to describe change of pull-off force with respect to the total machined linear distance (160 μm for each pattern machined), regardless of whether the machined trench is visible or not. The pull-off forces during the initial tip wear region and the tip failure region for all the machining conditions (Figs. 6–8) were analyzed. Fig. 10 is an example for one
Table 1 Factorial design experiments for tip wear study. Test number
Setpoint force/nN
Feed rate/μm/s
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
nanomachining process efficiently with the minimized cost. As can be seen in Figs. 6–8, the change rates of the pull-off force and the machined feature depth were different under different machining conditions. To model the effect of different process conditions on the tip wear rate, we need to study and quantify the change rate of the pull-off force and compare the results among different machining conditions. In Fig. 9, to ease the observation of the evolution of the tip wear and the resulting feature depth at each wear stage, the pull-off force and the corresponding feature depth (x10) for one machining condition (i.e. 1000 nN setpoint force and 2 μm/s feedrate) were plotted together. Based on the change rate of the pull-off force and the machined feature depths, the nanomachining process can be roughly divided into three regions: Initial tip wear region, Transition region, and Tip failure region. The initial tip wear region starts when a new tip begins to be used for nanomachining and produces the first few patterns. In this region,
Fig. 6. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 500 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance. 52
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Fig. 7. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 1000 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance.
machining condition as given in Fig. 9. The regression models fit the data very well with goodness of fit R-squared at about 0.95. The X axis is the machined linear distance (μm), while the Y axis is the pull-off force (nN) measured after each machined pattern. For both the initial tip wear region and tip failure region, linear regression lines were fitted to correlate the pull-off forces with the machined linear distance under each machining condition, which were defined as the local fitting lines. The slopes of these local fitting lines represent the change rate of the pull-off forces with respect to the machined distance. For the initial tip wear region, the intercepts of the line with the vertical axis represent the extrapolated pull-off force for a brand-new tip before machining. Table 2 lists the change rates of the pull-off forces for the initial tip wear region and tip failure region under all nine different machining conditions. Clearly, the change rates of the pull-off forces (that reflects the wear rate of the tip) increased with a larger setpoint force and a larger feed rate. This behavior is easy to understand, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Moreover, for each machining condition, the tip wear rate is much higher (more than three times) at the tip failure region than that from the initial tip wear region. The difference in the wear rate for these two regions can be explained intuitively. At the initial tip wear region, the tip is sharp enough to indent into the sample’s surface for mechanical machining. Both the bottom of the tip and the side of the tip interact with the sample. Tip wear happens at the bottom surface and the side surface, although the wear rate at the bottom tends to be larger. The tip wear on the side surface sharpens the tip and effectively reduces the increase rate of the tip radius. At the tip failure region, the tip is too blunt to be indented into the sample to have effective machining. The tip simply slides on the sample, and the resulting tip wear mainly
happens at the bottom surface, which rapidly increases the tip radius, as indicated by the large change rate of the pull-off force. To study the effect of the machining conditions (i.e. setpoint force and feedrate) on the tip wear, process models were developed to describe the progression of the tip wear and estimate the transition between the initial region and the tip failure region. The transition from the initial region to the tip failure region is an indicator of tip life. From Figs. 6–8, approximately linear trends are observed for the tip wear with respect to the machined distance for both the initial region and the tip failure region. Linear regression models were developed to correlate the pull-off forces with the machined linear distance during both the initial tip wear region and tool failure region. In the linear regression model, both the slope and intercept were correlated with machining parameters that were setpoint force and feedrate, which was shown as Eq. 1.
Fp = slope × Dlinear +intercept k2 F slope = k1 ⎛ set ⎞ feed k3 + k 4 ⎝ 1000 ⎠ k6 F intercept= k5 ⎛ set ⎞ feed k7 + k8 ⎝ 1000 ⎠
(1)
Fp is the pull-off forces measured during the initial tip wear region (nN), Dlinear (μm) is the machined linear distance, Feed is the feed rate (μm/s), Fset is the applied setpoint force (nN). In the model, the setpoint force is scaled (by a factor of 1000) to make the value of the force and feedrate of comparable magnitude. slope and intercept represent the slope and intercept of the linear regression model where k1, k2, k3, k 4 and k5 are regression coefficients. With the pull-off forces and corresponding 53
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Fig. 8. Measured pull-off forces and the average depth of the machined trenches as a function of the number of machined patterns when the setpoint force is 1500 nN at three feedrates (1, 2, 3 μm/s). In the plots, one pattern is 160 μm in machining distance.
Fig. 9. The measured pull-off forces and the feature depth (x10) as a function of the number of machined patterns. The blue dots represent the machined feature depth, while the boxes represent the pull-off forces. Based on the rate of change of pull-off forces or feature depth, the tip wear process can be identified as three regions: Initial tip wear region, transition region and tip failure region. In the plot, one pattern is 160 μm in machining distance. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 10. The plot of both global fitting line and local fitting line for (a) initial tip wear region and (b) tip failure region when the setpoint force and feed rate were set as 1000 nN and 2 μm/s. The blue dots represent the measured pull-off forces, the red lines represent the local fitting lines and the green lines represent the global fitting lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
machining parameters applied, this regression model was calibrated and all the coefficients were determined, as shown in Eq. 2.
Finitial = slope *Dlinear +intercept 2.76 F slope=0.019*⎛ set ⎞ *feed 0.23 + 0.0083 ⎝ 1000 ⎠ intercept = 26.56
(2)
the tips used in the experiments before machining. Similarly, a regression model was developed and calibrated for the tip failure region, which is shown as Eq. 3.
As can be observed, the slope (or the wear rate) increases with the larger setpoint force and feed rate, and the intercept is calculated as 26.56 nN, which is close to the average of measured pull-off forces for 54
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indication of tip life for that specific machining condition. From the regression model, two lines were used to describe the evolvement of tip wear along with the machined distance. The intersection point of the two lines for both the initial tip wear region and tip failure region provided an estimation of the transition point, from which the change rate of the pull-off force increased rapidly. The specific machined linear distance at the intersection point can also be calculated from Eqs. 2 and 3 when the calculated Ffailure equal to Finitial at each machining condition. Fig. 11 provided an example showing the lines from regression model (with goodness of fit R-squared at 0.93) and the transition point indicating the tip life when the setpoint force and feed rate were 1000 nN and 2 μm/s respectively. Table 3 listed the estimated transition point in both the machined distance and the number of the machined patterns. The machined feature depth around the transition point was also listed in Table 3. Compared with the evolvement of the tip wear from Figs. 6–8, these transition points from the regression model provided a rough estimation about of tip life about when to change the tip to achieve acceptable machining performance.
Table 2 The wear rate measured by the change rate of the pull-off forces (nN/ μm) for the initial tip wear region and tip failure region under different machining conditions. Test #
Setpoint force (nN)
Feed rate (μm/s)
Wear rate at initial region (nN/μm)
Wear rate at failure region (nN/μm)
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
0.0097 0.0083 0.0185 0.0209 0.0289 0.0382 0.0691 0.0755 0.0819
0.0292 0.0426 0.0667 0.0623 0.1123 0.1379 0.1528 0.1971 0.2519
6. Summary In this work, the tip wear during the tip-based vibration-assisted nanomachining process was systematically studied, and process models were developed to understand the correlation between process conditions and corresponding tip life. In addition to the SEM inspection, the pull-off force between the AFM tip and the sample surface was applied to measure the tip wear directly without unloading the tip from the AFM and without interupting the machining process. To study the effect of the machining parameters, such as setpoint force and feed rate, on the tip wear process, a full factorial design experiment was performed to study the effects of these machining parameters on the tip wear, and to develop the tip wear models to estimate the tip life and the machined distance for the tip change. During the nanomachining process, three regions (i.e. initial tip wear region, transition region, and tip failure region) were observed as the tip was gradully worn off. The wear rate of the tip was higher with a larger setpoint force and a larger feed rate, as a larger machining load resulted in more severe wear of the tip used in the nanomachining. Moreover, the tip wear rate was much high at the tip failure region than that from the initial tip wear region. Regression models were developed to describe the tip wear behavior for the initial tip wear region and the tip failure region, which provided a methodology to estimate the tip life (i.e. when the tip needs to be changed). The model of the tip wear and tip life can potentially provide usefully guidelines for future proces planning and process optimization.
Fig. 11. Lines from regression model for the initial regions and tip failure region. The intersection of the two lines provide an estimation of the transition point where the tip need to be changed. Table 3 The estimated transition point from the regression model and the feature depth at the transition point. Test #
Setpoint force (nN)
Feed rate (μm/s)
Machined distance (μm)
Number of patterns machined
Machined feature depth (nm)
1 2 3 4 5 6 7 8 9
500 500 500 1000 1000 1000 1500 1500 1500
1 2 3 1 2 3 1 2 3
1328 1456 1504 2032 1952 1872 2624 2320 2208
8.3 9.1 9.4 12.7 12.2 11.7 16.4 14.5 13.8
5 2 1 6 2 1 4 4 5
Ffailure = slope *Dlinear +intercept 1.78 F slope=0.066*⎛ set ⎞ *feed 0.55 − 0.0089 ⎝ 1000 ⎠ 1.72 F intercept=-101.57*⎛ set ⎞ *feed 0.62 + 33.66 1000 ⎝ ⎠
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(3)
where Ffailure is the pull-off force measured during the tip failure region (nN). Similar to the regression model for the initial tip wear region, the slope (or wear rate) increases with the larger setpoint force or feed rate. In Fig. 10, the pull-off forces were plotted versus the machined linear distance when the setpoint force and feed set as 1000 nN and 2 μm/s separately. As can be observed, for both the initial tip wear region and tip failure region, the lines from the regression models fits well to the changing trend of the measured pull-off force. Using the regression model, the life of the tip can be roughly estimated as well. For the tip-based nanomachining process, the patterns machined during the initial tip wear region are usually acceptable, while during the tip failure region, the tip rapidly wears out, and no trenches can be reliably machined. To provide the guideline for the tip change, we need to identify a machined distance where pattern quality rapidly degrades, which is termed as the “transition point” and is an
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