# Independent Study

## Independent Study

**Independent Study Bond Parameters Optimization in Thermosonic Copper Wire Bonding Process in Semiconductor Industry Khrongkhet Khuantham An Independent Study submitted in Partial Fulfillment of the requirements for the Degree of Master of Engineering (Engineering Management) International Graduate Program, Kasetsart University 2009**

Khrongkhet Khuantham 2009 : Bond Parameters Optimization in Thermosonic Copper Wire bonding Process in Semiconductor Industry. Master of Engineering (Engineering Management), Department of Industrial Engineering. Independent Study Advisor : Associated Professor Prapaisri Sudasna-na Ayudthya, Ph.D.

54 pages. This independent study relates to the thermosonic copper wire bonding process used for providing electrical connection between the silicon chip and the external leads of the semiconductor device using copper bonding wires on package type Heat Sink Thin Shrink Small Outline 32 (HTSSOP32) that has total 32 leads fingers. The objective of the study was to identify the significant factors affecting low average stitch pull test and determine the optimum bond parameters necessary to maximize the stitch pull test level. The statistical design of experiment was applied to first indentify the significant factors.

The critical process parameters likely to create the low average stitch pull test level were defined by the process engineer. The factors chosen for study were contact time, contact power, contact force, base time, base power and base force parameter. A 26-1 Factorial design was used to evaluate these. The result indicated the significant factors to be contact force, base power and base force.

The optimum levels of the three significant factors were determined by using a Central Composite Design and Canonical Analysis. The result showed the stationary points for the average stitch pull test surface. The optimum level of the factors are contact force of 218 grams-force ; base power of 86 DAC ; base force of 268 grams force. The resulting average stitch pull test was 28.0764 grams force. _ _ Student’ s Signature Advisor’ s Signature DD MM YY

i TABLE OF CONTENTS Page TABLE OF CONTENTS (i) LIST OF TABLES (ii) LIST OF FIGURES (iii) INTRODUCTION 1 OBJECTIVES 5 LITERATURE REVIEWS 7 FACTORS SCREENING EXPERIMENT 12 RESPONSE SURFACE METHOD 14 RESPONSE SURFACE ANALYSIS 18 MATERIALS AND METHODS 22 RESULTS AND DISCUSSION 24 FRACTIONAL FACTORIAL RESULTS 25 CENTRAL COMPOSITE RESULTS 30 CONCLUSION 38 RECOMMENDATIONS 39 REFERENCES 40 APPENDICES APPENDIX A 43 APPENDIX B 45 APPENDIX C 50

ii LIST OF TABLES Page Table 1 : Physical properties comparing between Cu Al and Au wire 8 Table 2 : Advantages of copper wires over gold wires 9 and challenges of wire bonding using copper wires Table 3 : Matrixes of 2k Central Composite design 15 Table 4 : α and n0 for experiment that have various factors 16 Table 5 : Process parameters with different operating levels 22 Table 6 : 26-1 Factorial Design of Experiment 24 Table 7 : Result of response from 26-1 Factorial Experiment 25 Table 8 : Level of factors for 23 Central Composite Design experiment 30 Table 9 : Result of response from 23 Central Composite design 30

iii LIST OF FIGURES Page Figure 1 : Wire bonding characteristics 1 Figure 2 : Stitch bond timing diagram 3 Figure 3 : Process Capability of Stitch pull test 4 Figure 4 : Wire bonding diagram of HTSSOP32 Package 6 Figure 5 : HTSSOP32 sample bonded device 6 Figure 6 : Wire bond machine ASM Eagle60AP 6 Figure 7 : MKE copper wire size 38 um 6 Figure 8 : PECO capillary for copper wire size 38 um 6 Figure 9 : Wire bonding mechanism 7 Figure 10 : Copper wire bonding 8 Figure 11 : EFO assembly for gold and copper wire 9 Figure 12 : Crack Stitch neck and wire pull test schematic 10 Figure 13 : Wire pull tester 11 Figure 14 : Pull hook 11 Figure 15 : Total points in 23 Central Composite design 15 Figure 16 : Contour plot analysis 18 Figure 17 : Response surface of second-degree polynomial 20 of 2 and 3 factors that optimum point is maximum point Figure 18 : Response surface of second-degree polynomial 20 of 2 and 3 factors that optimum point is minimum point Figure 19 : Response surface of second-degree polynomial 21 of 2 and 3 factors that optimum point is saddle point Figure 20 : Pareto effect plot on factors that influence 26 the average stitch pull test

iv LIST OF FIGURES (Continued) Page Figure 21 : Normality Probability Plot of average stitch pull test 26 (Screening experiment) Figure 22 : Residual Plot of average stitch pull test 27 (Screening experiment) Figure 23 : Main Effects Plot for average stitch pull test, contact force 28 Figure 24 : Main Effects Plot for average stitch pull test, base power 28 Figure 25 : Main Effects Plot for average stitch pull test, base force 29 Figure 26 : Normality Probability Plot of average stitch pull test 31 (Central composite experiment) Figure 27 : Residual Plot of average stitch pull test 32 (Central composite experiment) Figure 28 : Contour plot of response surface equation of the average 33 stitch pull test that consists of base power and contact force Figure 29 : Surface plot of response surface equation of the average 34 stitch pull test that consists of base power and contact force Figure 30 : Contour plot of response surface equation of the average 34 stitch pull test that consists of base force and contact force Figure 31 : Surface plot of response surface equation of the average 35 stitch pull test that consists of base force and contact force Figure 32 : Contour plot of response surface equation of the average 35 stitch pull test that consists of base force and base power Figure 33 : Contour plot of response surface equation of the average 36 stitch pull test that consists of base force and base power Figure 34 : Process Capability from the optimized bond parameters level 37

v LIST OF FIGURES (Continued) Page Appendices Appendix B-1 Create Factorial Design 45 Appendix B-2 Select design level and number of factor 45 Appendix B-3 Select ½ fractional and number of replication 46 Appendix B-4 Input factor name and level 46 Appendix B-5 Input response, average stitch pull test 47 Appendix B-6 Analyze the data 47 Appendix B-7 Select the response and order of term in model 48 Appendix B-8 Select Normality plot and residual plot 48 Appendix B-9 Pareto Chart of effect plot 49 Appendix C-1 Create Response Surface Design 50 Appendix C-2 Select design type and number of factor 50 Appendix C-3 Select number of center point, replication and alpha level 51 Appendix C-4 Input factor name and level 51 Appendix C-5 Input the response, average stitch pull test 52 Appendix C-6 Analyze the data 52 Appendix C-7 Select the response and model term 53 Appendix C-8 Minitab output 53 Appendix C-9 Create the surface and contour plot 54

Bond Parameters Optimization in Thermosonic Copper Wire bonding Process in Semiconductor Industry Introduction Semiconductor industry is a highly competitive business with ever shorter product life cycles. Therefore, one of challenges in this industry is quality improvement with no additional investment. Highly competitive market has made semiconductor manufacturers focus on their quality improvement activities in order to get the highest rate of yield on expensive spending. In the past, the way to obtain the high quality integrated circuit (IC) was product inspection after completion of a production process.

This method tends to solve problems after they had already occurred. It could not really solve the root cause of the problem and ensure the quality level of products so Statistical Process Control (SPC) is a concept about prevention and problem solving, which concentrates in process control and improvement at the production process directly. SPC is applied through answering a series of questions, such as “How to collect the data?”, “How to analyze the data and interpret the output result in order to indicate the product quality level. It can help to collect the data from the controlled process and analyze them in order to find out the process capability.

**While a process engineer is trying to improve the process capability, one of the statistical method that is applied is experimental design. Next step of research and analyze the data to find out the factor effect and constraints in the process as using the advance statistical method is called Response Surface Methodology, RSM.**

Manufacturing processes of IC chippackage consist of a taping, lapping, detaping, wafer mounting, wafer sawing, die attaching, epoxy curing, wire bonding, molding, marking, trimming, solder plating, forming, testing and packing. The purpose of wire bonding is to use gold wires to provide electrical connection between the silicon chip and the external leads of the semiconductor device see Figure 1. Figure 1 Wire bonding characteristics

2 In recent years, Copper is rapidly gaining a foothold as an interconnection material in semiconductor packaging because of its obvious advantages over gold.

These advantages include: 1) cost reduction of up to 90%; 2) superior electrical and thermal conductivity; 3) less inter-metallic growths; 4) greater reliability of the bond at elevated temperatures; and 5) higher mechanical stability. Although Cu wire has many advantages over Au wire, it has not been widely used like Au wire, because it also brings many new challenges and quality problems to wire bonding.

Problem Statement This research study in Thermosonic copper wire bonding process because of the material cost saving activity, copper were substituted for gold, which is the predominant material used for plastic semiconductor package interconnections. The development of a copper wire bonding process represents a significant step towards the goal. However, semiconductor manufacturers still face problems on the low yield rate of copper wire bond products concerning to poor bond ability of stitch bond (one of copper wires bonding challenges) because they had just been finished the qualification, testing and small scale pilot production.

The problem of Cu wire bonding process that would be studied is low stitch pull test (SPT) value of copper wire bonding, which is frequently found from copper wire bonding process as there are six factors to be studied and analyzed, which are 1. Contact time, which is time that occurred after capillary impact to lead surface. This period contact power and force are applied. If contact time is set to be zero, contact parameter will be ignored.

2. Contact power, which is ultrasonic energy that is applied during contact time in order to prepare stitch shape to be proper before Base parameter would be applied. 3. Contact force, which is force that is applied during contact time in order to prepare stitch shape to be proper before Base parameter would be applied. 4. Base time, which is main time that used after contact time is completed for applying base power and force. 5. Base power, which is main ultrasonic energy that is applied during base time. It is the main factor for stitch bond quality. 6. Base force, which is main force that is applied during base time after contact period.

It is also the main factor for stitch bond quality. These interest factors are illustrated in stitch bond timing diagram in Figure 2.

**3 Figure 2 Stitch bond timing diagram Currently these six factors that might influence to the low stitch pull test problem are set as the following level, 1. Contact Time is set at 5 ms 2. Contact Power is set at 35 DAC 3. Contact Force is set at 240 gf 4. Base Time is set at 30 gf 5. Base Power is set at 100 DAC 6. Base Force is set at 290 gf**

4 These bond parameters give poor process capabilities, both low mean of stitch pull test and low Cpk, 0.69 bring about to high reject quantity of stitch pull test lower than specification (15 gf) were observed as shown in process capability Figure 3 below, Figure 3 Process Capability of Stitch pull test

5 Objectives 1. To study in thermosonic copper wire bonding process to select the significant factors that influence to the low Stitch pull test level that directly effect to the low Cpk of stitch pull test as using 2k Fractional Factorial Design of experiment. 2. To optimize response, which is average stitch pull test, from the significant factors that have the effects in thermosonic copper wire bonding process from Response surface method. 3. To find the average stitch pull test equation from Response Surface Methodology as follow to 2k Central Composite.

4. To improve average stitch pull test level and Cpk in thermosonic copper wire bonding process.

Independent Study Scope This research would study in integrated circuit (IC) package type Heat Sink Thin Shrink Small Outline (HTSSOP32) that has total 32 leads fingers as all devices are prepared from only one Die bond machine type ASM AD830. During thermosonic copper wire bonding process, this research would consider only average stitch pull test value from total wires, 3 units x 35 wires/unit (wire bonding diagram and sample bonded device of HTSSOP32 is illustrated in Figure 4 and Figure 5), which is 105 wires per run as there are research scope as the following, 1. Wire bonding machine type ASM Eagle60AP , shown in Figure 6.

2. Lead frame type is stamped CuNiPd(Au) 32 leads. 3. Epoxy type using at Die bond is QMI-519. 4. Die is ICN8 wafer fabrication. 5. Copper wire type is MKE Cu wire size 38 um, shown in Figure 7. 6. Capillary type is PECO copper wire capillary for 38 um wire size. See Figure 8.

7. Wire bonding temperature is 230 C 8. Wire Pull Test machine type is BT4000.

6 Figure 4 Wire bonding diagram Figure 5 HTSSOP32 of HTSSOP32 Package sample bonded device Figure 6 Wire bond machine ASM Eagle60AP Figure 7 MKE copper wire Figure 8 PECO capillary size 38 um for copper wire size 38 um

7 Literature review Manufacturing processes of IC chippackage consist of a taping, lapping, detaping, wafer mounting, wafer sawing, die attaching, epoxy curing, wire bonding, molding, marking, trimming, solder plating, forming, testing and packing.

The purpose of wire bonding is to use gold wires to provide electrical connection between the silicon chip and the external leads of the semiconductor device. Tung-Hsu Hou et al., (2005) explained in an integrated system for setting the optimal parameters in IC chip-package wire bonding processes that the wire bonding process starts from positioning the capillary above a bond pad of the die with a gold ball formed at the end of the gold wire. The capillary then descends and presses the gold ball on the bond pad to form the first bond that is also called the ball bond. In a thermo-sonic wire bonding system, ultrasonic power and vibration force are applied, and heat is also applied to the pad to facilitate the bonding efficiency.

After the ball is bonded to the die, the capillary arises to the loop height position. The gold wire is then led by the capillary to the inner lead of the substrate. The capillary deforms the wire against the lead, producing a wedge-shaped bond that is called the stitch bond or the second bond. The capillary then arises to the preset height and a new wire ball is formed on the tail of the gold wire. A hydrogen flame or an electric frame may be used to form the ball. The wire bonding mechanism is illustrated in Figure 9. Figure 9 Wire bonding mechanism In recent years, Copper is rapidly gaining a foothold as an interconnection material in semiconductor packaging because of its obvious advantages over gold.

These advantages include: 1) cost reduction of up to 90%; 2) superior electrical and thermal conductivity; 3) less inter-metallic growths; 4) greater reliability of the bond at elevated temperatures; and 5) higher mechanical stability.

8 Copper is inherently 3 to 10 times cheaper than gold, so substituting gold wires with copper wires can realize tremendous annual cost savings for a semiconductor packaging company. See Figure 10, Copper wire bonding. Figure 10 Copper wire bonding Copper wire, with an electrical resistivity of 0.017 micro ohm-m at room temperature, is more electrically conductive by about 25%-30% than gold, which has a resistivity of 0.022 micro-ohm-m at room temperature. This low electrical resistivity of copper results in better electrical performance. In particular, copper wire is a preferred bonding wire material for high-current or high-power applications, since it can carry more current for a given wire diameter.

Copper also has about 25% higher thermal conductivity than gold (385-401 W m-1 K-1 for Cu and 314-318 W m-1 K-1 for Au). Thus, copper wires dissipate heat within the package faster and more efficiently than gold wire, minimizing the thermal stress to which they are exposed. Excessive heat on the wires can promote grain growth, which lowers the strength of the wires. The heat-affected zone (HAZ) formed on the wire during free air ball formation also tends to be shorter in copper wires because of their better thermal conductivity. The shorter HAZ in copper wires give them better wire looping capability than gold, an important aspect of die stacking.

See physical properties comparing with Au wire in table 1.

(http://www.siliconfareast.com/wirebond-copper.htm) Table 1 Physical properties comparing between Cu Al and Au wire Although Cu wire has many advantages over Au wire, it has not been widely used like Au wire, because it also brings many new challenges and quality problems to wire bonding. See advantages and challenges of copper wire bonding in table 2,

9 Table 2 Advantages of copper wires over gold wires and challenges of wire bonding using copper wires - - Advantages Challenges - - Lower cost Easy to be oxidized in air Better thermal properties Additional bonding parameters for using Better electrical properties forming inert gas Excellent ball neck strength Need more ultrasonic energy and higher bonding Higher Stiffness force, which can damage Si substrate High loop stability Poor bond ability for stitch/wedge bonds - - Copper is easy to be oxidized in air, and therefore copper wire bonders must have additional tools to prevent copper oxidation (Hong et al., 2005).

Additional bonding parameters for using forming inert gas need to be optimized (Chen et al., 2006), and additional cost of forming gas must be considered (Goh and Zhong, 2007). Although N2 gas can be a suitable option, a forming gas mixture of 95 per cent N2/ 5 per cent H2 has been shown to be the best choice (England and Jiang, 2007), see figure 11 illustrated additional tool of inert gas for copper wire bonding comparing with gold wire bonding.

EFO assembly for gold wire EFO assembly with forming gas supply for copper wire Figure 11 EFO assembly for gold wire and EFO with forming gas supply for copper wire Copper wires have much higher hardness and stiffness than gold wires. Copper wire bonding needs more ultrasonic energy and higher bonding force, which can damage the Si substrate, form die cratering (Hong et al., 2005) and induce cracking and peeling of the bonding pad (Tian et al., 2005). A stage temperature of 150-2008C is also needed for bonding copper wire (Chen et al., 2006). As-drawn copper wire possesses higher strength and hardness, but its lower ductility reduces the reliability of bonding.

The lower strength of the

10 annealed wire results in breakage (Hung et al., 2006). There is also a need to investigate the effects of the process parameters on the hardness of Cu FABs (Zhong et al., 2007), because Cu exhibits a larger strain-hardening effect at a higher strain rate Bhattacharyya et al., 2005). Since copper wire is harder than gold wire, to improve stitch Bond ability, higher parameter settings have to be used, causing heavy cap marks and potential short tails or wire open. Cu/Au stitch bonds are weak, and thus copper wire bonding has wire open and short tail defects, poor process control, and low stitch pull readings according to weak stitch neck strength from the improper bond parameters, see Figure 12 (weak stitch neck strength and wire pull test schematic used for stitch neck strength measurement) (Goh and Zhong, 2007).

Weak and Crack stitch neck Wire pull test Schematic effect to low pull test reading Figure 12 Weak stitch neck is the severing of the wire from its wedge or crescent bond due to a fracture in the neck. The stitch neck is the portion of the wire where the wire tapers off into the wedge or crescent bond. It is equivalent to the neck of a ball bond. Weak of stitch neck is commonly due to poor wire bonder set-up. Poor set-up includes improper bonding parameter settings, bond head movement settings, and worn-out or contaminated tools. Incorrect bonding parameters can deform the bond excessively, resulting in a very thin and weak heel which can easily fracture.

Improper bond head movements and low loop settings may subject the wires to excessive stresses that tend to pull them backward and away from the bonds, resulting in gross heel cracks which may propagate into total fracture. Worn-out and contaminated tools can also produce mechanical damage or defects in the wires which can act as starting points for crack propagation. The most likely potential cause of weak and crack stitch neck problem in thermosonic copper wire bonding process that has always been observed is the improper 2nd bond parameter because the copper wire characteristics is harder than gold, in order to make the wire stick on the lead surface the bond parameter have to

11 be set at the different or higher level than normal that used for gold wire. Therefore if these parameters is set at the improper level, they might affect directly to the weak of stitch neck problem. In general, the ways to measure or observe the stitch neck quality are scanning electron microscope (SEM) inspection, which is the very high power microscope, it is used to inspect at the neck of each interested wire. Another way is the stitch pull test (SPT), which is one of several available time-zero tests for wire bond strength and quality. It consists of applying an upward force under the wire to be tested, effectively pulling the wire away from the lead surface.

Stitch pull testing requires a special equipment commonly referred to as a wire pull tester (or bond pull tester) see Figure 13, which consists of two major parts: 1) a mechanism for applying the upward pulling force on the wire using a tool known as a pull hook see Figure 14; and 2) a calibrated instrument for measuring the force at which the wire eventually breaks. This breaking force is usually expressed in grams-force.

Figure 13 Wire pull tester Figure 14 Pull hook

12 Design of Experiment Design of experiment was started to use in Agriculture, which Montgomery (1976) indicated that Fisher had applied the Statistical method and data analysis to use on the experiment result at Agriculture laboratory in London, UK. This method is the Analysis of Variance (ANOVA), which is the main method for data analysis from experimental design but Fisher‘s concept could be well applied in researches of other study areas. Next step of research and analyze the data to find out the factor effect and constraints in the process as using the advance statistical method is called Response Surface Methodology, RSM., which Khuri and Cornell (1987) stated that there were researches in this area since 1930.

Response Surface method was achieved and popular, moreover it was applied in other research area such as Engineering, Food Sciences, Biology and Industry etc.

There are many applications of Design of experiment in researches in semiconductor industry but the mostly found and published by Sheffer and Levine (1991) is the research for study the stitch neck broken wires problem in wire bonding process as there was screening experiment to find out the significant factors from 5 factors by Fractional Factorial design of experiment. After that Sheffer and Levine (1991) stated about the method to find the surface response equation from the screening significant factors to improve process capability with Central Composite Design of experiment.

This research’ s purpose is to study the factors that influence to the response in thermosonic copper wire bonding process.

After finished wire bonding, next steps is the quality assurance of wire bonding as one important point for roving inspection is the stitch pull test, which is the procedure to check and ensure the stitch neck strength that have no any crack or weak condition. If there is any device that stitch pull test is lower than specification, that device and also the affecting production lot will be defective and would be scrapped all.

Screening Experiment Experiments, which consist of several interesting factors (Multi-factor experiments) have been widely used to screen the significant factors that influence to the response are the following, (http://www.siliconfareast.com/factorial-2k.htm ) 2k Factorial Experiment A frequently used Factorial Experiment design in the semiconductor industry is known as the 2k Factorial design, which is basically an experiment involving k factors, each of which has two levels ('low' and 'high'). In such a multi-factor twolevel experiment, the number of treatment combinations needed to get complete results is equal to 2k .

Thus, a 2k Factorial Experiment that deals with 3 factors would require 8 treatment combination, while one that deals with 4 factors would require 16 of them. One can easily see that the number of runs needed to complete a Factorial Experiment, even if only two levels are explored for each factor, can become very large.

13 The first objective of a Factorial Experiment is to be able to determine, or at least estimate, the factor effects, which indicate how each factor affects the process output. Factor effects need to be understood so that the factors can be adjusted to optimize the process output. The effect of each factor on the output can be due to it alone (a main effect of the factor), or a result of the interaction between the factor and one or more of the other factors (interactive effects). When assessing factor effects (whether main or interactive effects), one needs to consider not only the magnitudes of the effects, but their directions as well.

The direction of an effect determines the direction in which the factors need to be adjusted in a process in order to optimize the process output. In Factorial designs, the main effects are referred to using single uppercase letters, e.g., the main effects of factors A and B are referred to simply as 'A' and 'B', respectively. An interactive effect, on the other hand, is referred to by a group of letters denoting which factors are interacting to produce the effect, e.g., the interactive effect produced by factors A and B is referred to as 'AB'. Each treatment combination in the experiment is denoted by the lower case letter(s) of the factor(s) that are at 'high' level (or '+' level).

Thus, in a 2-Factorial Experiment, the treatment combinations are: 1) 'a' for the combination wherein factor A = 'high' and factor B = 'low'; 2) 'b' for factor A = 'low' and factor B = 'high'; 3) 'ab' for the combination wherein both A and B = 'high'; and 4) '(1)', which denotes the treatment combination wherein both factors A and B are 'low'.

Based on Factorial Experiments, the main effect of a factor A in a two-level two-factor design is the change in the level of the output produced by a change in the level of A (from 'low' to 'high'), averaged over the two levels of the other factor B. On the other hand, the interaction effect of A and B is the average difference between the effect of A when B is 'high' and the effect of A when B is 'low.' This is also the average difference between the effect of B when A is 'high' and the effect of B when A is 'low.' The magnitude and polarity (or direction) of the numerical values of main and interaction effects indicate how these effects influence the process output.

A higher absolute value for an effect means that the factor responsible for it affects the output significantly. A negative value means that increasing the level(s) of the factor(s) responsible for that effect will decrease the output of the process. 2k Fractional Factorial Experiment This experiment has same characteristic as 2k Factorial Experiment but it will be used when there are many factors to study in the experiment. Due to the experiment would be high cost and also waste much time in order to perform the experiment until complete all treatment combination. Therefore they might perform the experiment as half replication or quarter replication or less.

14 Response Surface Methodology In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The method was introduced by G. E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a set of designed experiments to obtain an optimal response. Box and Wilson suggest using a first-degree polynomial model to do this. They acknowledge that this model is only an approximation, but use it because such a model is easy to estimate and apply, even when little is known about the process. An easy way to estimate a first-degree polynomial model is to use a Factorial Experiment or a fractional Factorial designs.

This is sufficient to determine which explanatory variables have an impact on the response variable(s) of interest. Once it is suspected that only significant explanatory variables are left, then a more complicated design, such as a central composite design can be implemented to estimate a seconddegree polynomial model, which is still only an approximation at best. However, the second-degree model can be used to optimize (maximize, minimize, or attain a specific target for) a response.

To estimate the second-degree polynomial model, popular and widely used design of experiments are 2k Central Composite design and Box-Behnken Design Regarding to this research considers in second-degree polynomial model as Y = β0 + i Xi + i Xi 2 + ij XiXj + ε i

15 (X1, X2,..., Xk) = (0,0,...,0) 3. Star points number is equal to 2k as each point is far from the center points with same distance, which is α as α = (2k )1/4 will be the rotatable design. Star points experiment is written as (±α,0,0,...,0), (0,±α,0 ( 0,0,...,±α) Therefore 2k Central Composite design will have total points equal to 2k + 2k + n0.

This experiment can be illustrated in Figure 15, and matrixes of 2k Central Composite design is illustrated in table 3. Figure 15 Total points in 23 Central Composite design Table 3 Matrix of 23 Central Composite design X1 X2 X3 Points -1 -1 -1 1 -1 -1 -1 1 -1 1 1 -1 -1 -1 1 1 -1 1 -1 -1.682 0 0 1.682 0 0 0 -1.682 0 0 1.682 0 0 0 -1.682 0 0 1.682 0 0 0 Center Factorial Star

16 In order to make the design of experiment to be Rotatable design, which is variance of estimated response are constant at every point of experiment that is far from the center point in same distance, it have to be defined α and n0 properly as below, α = (2k )1/4 n0 = 0.8386 ((2k )1/2 +2) 2 – 2k -2k From many researches (Box and Draper (1987) found that Central Composite design is flexible to use and has the most efficiency when compare to the other design to estimate the second-degree polynomial model. Table 4 Show α and n0 for experiment that have various factors Factorial Points 4 8 16 32 64 Star Points 4 6 8 10 12 Center Points 5 6 7 10 15 No.of Runs 13 20 31 52 91 α 1.414 1.682 2 2.378 2.828 Box-Behnken design Box-Behnken design is developed by Box and Behnken (1960) to estimate the second-degree polynomial model, which combines 2k Factorial and Randomized Incomplete block and it has the property of Rotatable as each block are orthogonal to each other.

This experiment has efficiency when is used to study in 3-level design, it is better than 3k Full Factorial. Significant criticisms of RSM include the fact that the optimization is almost always done with a model for which the coefficients are estimated, not known. That is, an optimum value may only look optimal, but be far from the truth because of variability in the coefficients. A contour plot is frequently used to find the responses of two variables to find these coefficients by including a large number of trials in each and combinations of them, and using some sort of interpolation to find potentially better intermediate values between them.

But since experimental runs often cost a lot of time and money, it can also be difficult to pinpoint the ideal coefficients, as well; there are frequently strategies used to find those values with minimal runs. Experimental designs used in RSM must make tradeoffs between reducing variability and reducing the negative impact that can be caused by bias.

17 Therefore, this research proposes method for optimization of a response in thermosonic copper wire-bonding process, which is average stitch pull test level using Response surface method (2k Central Composite design) to estimate the seconddegree polynomial model. Lack of Fit Test Lack of Fit Test is used to test the model whether is fitting to the experiment result or not. The model would be used to estimate the response as set up the hypothesis below, H0 : Model is adequate or there is no Lack of fit. Ha : Model is not adequate or there is Lack of fit. However Lack of Fit test would be done under 3 constraints, which are 1.

Number of run must be greater than number of parameter that are shown in the model, N>P 2. There are at least 2 replications on 1 run or more runs but it can ignore the replications, if there is estimation on the variance of error from the analysis in previous experiment.

3. Error is assumed as below, ε ~ NID (0,σε 2 ) Lack of Fit Test is done under the 1st and 2nd constraint as separating SSE to be 2 parts, which are Lack of Fit and Pure Error. It can be calculated as the following, SS Pure Error lu Y Y l − ∑∑ = = SS Lack of Fit = SSE – SS Pure Error While l = order of runs, l =1, 2,..., n n = number of non-repeat runs u = order of replication in each run, u =1, 2,..., number of replications from order of run mean of run order l

18 Lack of Fit test uses F-test from formula, F = SS Lack of Fit/ (n-p) SS Pure Error/ (N-n) While (n-p) = degree of freedom of lack of Fit (N-n) = degree of freedom of Pure Error Response Surface Analysis We can estimate the second-degree polynomial model as determining levels of factors in order to get the optimum response by 2 methods, Method 1 ; Contour Plot analysis, which is illustrated in figure 16 Figure 16 Contour plot of response surface of 2 factors Method 2 ; Canonical Analysis From j i i j ij i k i ii ∑∑ Or β

19 While and 22 21 1 12 11 kk The point that consists of the level of factors that give the optimum response is called Stationary Point, which might be the point that give the minimum or the maximum level of response. Stationary Point could be calculated as Therefore is the optimum estimated response Canonical Analysis can test the optimum response whether it is the maximum or minimum or saddle point from Eigenvalue consideration as below, While { } i λ is Eigenvalue or Characteristic Roots of Matrix B This Optimum point could be tested by solving the above equation and considering the sign of { } i λ that are illustrated in figure 17, 18 and 19.

Moreover Canonical Analysis can transform the second-degree polynomial form to be Canonical form as there is the center point at ~ X as below, ... ˆ ˆ k kW While Canonical variable relates to the polynomial variable as While M is the orthogonal matrix from the relation below, λ

20 Figure 17 Response surface of second-degree polynomial of 2 and 3 factors that optimum point is maximum point, example i λ sign of 2 factors is - ) Figure 18 Response surface of second-degree polynomial of 2 and 3 factors that optimum point is minimum point, example i λ sign of 2 factors is + )

21 Figure 19 Response surface of second-degree polynomial of 2 and 3 factors that optimum point is saddle point, i λ sign is ) and - , 0)

22 Materials and Method Materials Materials that are used in this research consist of 1.

Devices that passed Die attaching process as no any defects and ready for wire bonding process. 2. Materials used in Thermosonic copper wire bonding process. 3. Wire pull test machine that would be used for perform the stitch pull test after device samples have already been bonded. 4. Personal Computer in laboratory at IGP Industrial engineering, Kasetsart University. 5. Minitab software version 15 that is used for data analysis. Method Method that is used in this research are 1. Perform the screening experiment the significant factors that influence to the average stitch pull test level as there are total 6 factors that illustrated in table 5.

26-1 Factorial design 1 replication was selected, there are total 32 runs as each run has 105 wires that are used to calculate the average stitch pull test level for each run.

Table 5 Process parameters with different operating levels - - Factor Unit Symbol Level -1 Level +1 - - Contact Time ms CT 5 15 Contact Power DAC CP 20 40 Contact Force gf CF 200 250 Base Time ms BT 25 35 Base Power DAC BP 85 100 Base Force gf BF 250 300 - - 2. Perform the stitch pull test on testing devices and record the stitch pull test level of each run and then calculate the average value in order to use for factors effect analysis from effect plot. 3. Use Minitab software to do effect analysis from Pareto effect plot to find out the significant factors that influence to the average stitch pull test level, show the interaction plot of 2 factors and also test the error assumption in stitch pull test data by Normal Probability plot and Residual plot.

4. Perform response surface method as using the 2k Central Composite design on the significant factors from (3.)

23 5. Use Minitab to analyze the data as using ANOVA and testing lack of fit and show the response surface figure and optimize the optimum point of response surface as using Contour plot and Canonical analysis.

24 Result and Discussion Design of Experiment (screening experiment) Experiment is conducted as 26-1 Factorial design on six factors to study their effects to the response, average stitch pull test level as shown in table 6, Table 6 26-1 Factorial Design of Experiment 26-1 Factorial Experiment Result After performing 26-1 Factorial Experiment, which has total 32 runs, testing and collecting average Stitch pull test data, the result is shown in Table 7

25 Table 7 Result of response from 26-1 Factorial Experiment From the average stitch pull test result from each run, testing to screen factors that influence to the average stitch pull was performed as using Pareto effect plot as shown in figure 20 and also Normal Probability plot and Residual plot were used to test the error assumption in stitch pull test data as shown in Figure 20 and 21. StdOrder RunOrder CenterPt Blocks CT CP CF BT BP BF Average STP 17 1 1 1 5 20 200 25 100 300 21.758 15 2 1 1 5 40 250 35 85 300 22.701 20 200 25 85 250 26.243 14 4 1 1 15 20 250 35 85 300 23.186 12 5 1 1 15 40 200 35 85 300 24.669 6 6 1 1 15 20 250 25 85 250 25.884 28 7 1 1 15 40 200 35 100 250 23.706 21 8 1 1 5 20 250 25 100 250 21.519 40 200 25 85 300 25.755 22 10 1 1 15 20 250 25 100 300 20.106 10 11 1 1 15 20 200 35 85 250 27.462 13 12 1 1 5 20 250 35 85 250 25.407 9 13 1 1 5 20 200 35 85 300 25.829 19 14 1 1 5 40 200 25 100 250 24.360 18 15 1 1 15 20 200 25 100 250 23.990 5 16 1 1 5 20 250 25 85 300 23.386 20 17 1 1 15 40 200 25 100 300 22.068 7 18 1 1 5 40 250 25 85 250 25.927 23 19 1 1 5 40 250 25 100 300 21.113 27 20 1 1 5 40 200 35 100 300 21.676 16 21 1 1 15 40 250 35 85 250 24.108 26 22 1 1 15 20 200 35 100 300 22.642 32 23 1 1 15 40 250 35 100 300 22.551 2 24 1 1 15 20 200 25 85 300 25.305 4 25 1 1 15 40 200 25 85 250 26.871 30 26 1 1 15 20 250 35 100 250 21.929 31 27 1 1 5 40 250 35 100 250 22.732 29 28 1 1 5 20 250 35 100 300 21.179 11 29 1 1 5 40 200 35 85 250 26.475 8 30 1 1 15 40 250 25 85 300 23.640 24 31 1 1 15 40 250 25 100 250 22.045 25 32 1 1 5 20 200 35 100 250 23.829

26 Figure 20 Pareto effect plot on factors that influence the average stitch pull test Figure 21 Normality Probability Plot of average stitch pull test

27 Normality Probability plot is shown as linear and Residual plot show Figure 22 Residual Plot of average stitch pull test Independence and equivalence of error distribution are around 0.0 along the observation order. Therefore we can conclude that stitch pull test error has normality distribution as independent and mean of error equals to zero and has the constant variance. From Testing effect of factors as using Pareto effect plot that is shown in Figure 20 found that there are only 3 main factors that has effect to the average stitch pull test level while no any interaction effects such two or higher order interaction.

Regarding to the result 3 factors that are significant are the following, 1. Contact force (CF), figure 22 shows contact force parameter that is set at 200 gf bring about to the average stitch pull test level higher than setting at 250 gf.

2. Base Power (BP), figure 23 shows base power parameter that is set at 85 DAC bring about to the average stitch pull test level higher than setting at 100 DAC. 3. Base Force (BF), figure 24 shows base force parameter that is set at 250 gf bring about to the average stitch pull test level higher than setting at 300 gf.

28 Figure 23 Main Effects Plot for average stitch pull test, contact force Figure 24 Main Effects Plot for average stitch pull test, base power

29 Figure 25 Main Effects Plot for average stitch pull test, base force 26-1 Factorial Experiment (Screening) Conclusion From the screening experiment of 26-1 Factorial design, found that the significant factors that have influence on the average stitch pull test level are contact force, base power and base force parameter.

Other factors, which are contact time, contact power and base time has no effect to the average stitch pull test level both main effect and interaction.

30 Design of Experiment (Response Surface Method) Experiment is conducted to find the response surface equation. Three considered factors are contact force (CF), base power (BP) and base force (BF) as the response still is the average stitch pull test level. The design experiment is 23 Central Composite design with 6 center points and 1 replication, total runs is 20 runs. Regarding to the result of screening experiment, contact time (CT), contact power (CP) and base time (BT) are not significant factors. Therefore, in Central Composite design of experiment we would control them as fixing their levels during experiment conducted while levels of 3 non-significant factors are still in the range of the previous screening as CT = 5 ms, CP = 30 DAC, BT = 25 ms.

Factor level that are used in the experiment is shown in Table 8. After performed the experiment, the average stitch pull test is shown in table 9.

Table 8 Show level of factors for 23 Central Composite Design experiment - - Factor Factor level - - -1.682 -1 0 1 1.682 - - CF -- Contact Force (gf) 183 200 225 250 267 BP -- Base Power (DAC) 79 85 92 100 105 BF -- Base Force (gf) 232 250 275 300 317 - - Table 9 Result of response from 23 Central Composite design StdOrder RunOrder PtType Blocks CF BP BF Average STP 1 1 1 1 200 85 250 26.571 4 2 1 1 250 100 250 21.726 8 3 1 1 250 100 300 20.985 15 4 0 1 225 92 275 26.56 10 5 -1 1 267 92 275 22.147 3 6 1 1 200 100 250 24.019 18 7 0 1 225 92 275 27.589 5 8 1 1 200 85 300 25.246 17 9 0 1 225 92 275 28.221 7 10 1 1 200 100 300 21.821 11 11 -1 1 225 79 275 26.685 2 12 1 1 250 85 250 25.644 14 13 -1 1 225 92 317 23.384 13 14 -1 1 225 92 232 24.319 16 15 0 1 225 92 275 26.402 19 16 0 1 225 92 275 27.733 20 17 0 1 225 92 275 28.05 9 18 -1 1 183 92 275 24.845 6 19 1 1 250 85 300 22.847 12 20 -1 1 225 105 275 24.574

31 From average stitch pull test analysis, found that the response surface equation of average stitch pull test contains both first and second-degree polynomial as significant in statistics and also Lack of Fit test result is not significant at 0.05 significant level as Minitab output showed the analysis result below, Analysis of Variance for Average STP Source DF Seq SS Adj SS Adj MS F P Regression 9 89.5027 89.5027 9.9447 11.74 0.000 Linear 3 31.4629 31.4629 10.4876 12.38 0.001 Square 3 57.8600 57.8600 19.2867 22.77 0.000 Interaction 3 0.1798 0.1798 0.0599 0.07 0.974 Residual Error 10 8.4720 8.4720 0.8472 Lack-of-Fit 5 5.5313 5.5313 1.1063 1.88 0.252 Pure Error 5 2.9408 2.9408 0.5882 Total 19 97.9747 Normal Probability plot and Residual plot that were used to test the error assumption in stitch pull test data showed independence and equivalence of error distribution are around 0.0 along the observation order.

Therefore we can conclude that stitch pull test error has normality distribution, see figure 25, as independent and mean of error equals to zero and has the constant variance, see figure 26. Figure 26 Normality Probability Plot of average stitch pull test

32 Figure 27 Residual Plot of average stitch pull test Therefore we can use this response surface equation to estimate the average stitch pull test level from the Estimated Regression Coefficients for Average STP that are given from Minitab as below, Estimated Regression Coefficients for Average STP Term Coef SE Coef T P Constant 27.4430 0.3754 73.104 0.000 CF -0.8049 0.2491 -3.232 0.009 BP -1.1208 0.2491 -4.500 0.001 BF -0.6322 0.2491 -2.538 0.029 CF*CF -1.5013 0.2425 -6.192 0.000 BP*BP -0.7470 0.2425 -3.081 0.012 BF*BF -1.3756 0.2425 -5.674 0.000 CF*BP 0.0246 0.3254 0.076 0.941 CF*BF -0.0019 0.3254 -0.006 0.996 BP*BF 0.1479 0.3254 0.454 0.659 S = 0.920436 PRESS = 47.8935 R-Sq = 91.35% R-Sq(pred) = 51.12% R-Sq(adj) = 83.57% The response surface equation of average stitch pull test is BF BP BF CF BP CF BF BP CF BF BP CF Y * 1479 .

0019 . * 0246 . 3756 . 1 7470 . 5013 . 1 6322 . 1208 . 1 8049 . 4430 . 27 =

33 From Canonical analysis, found that Eigenvalues are negative, which are 246973 . 4 , 914927 . 3 , 088009 Therefore response surface equation of average stitch pull test form is maximum as polynomial equation could be transformed to the Canonical equation as 246973 . 4 914927 . 3 088009 . 2 0764 . 28 Contour plot and surface plot of contact force, base power and base force factor that are illustrated in Figure 27, 28, 29, 30, 31 and 32 are used to optimize the maximum average stitch pull test level as using Minitab software.

We found that the level of factors that influence to the maximum average stitch pull test, which is 28.0764 grams-force are contact force at 218 grams-force, base power at 86 DAC and base force at 268 grams-force.

Figure 28 Contour plot of response surface equation of the average stitch pull test that consists of base power and contact force factor.

34 Figure 29 Surface plot of response surface equation of the average stitch pull test that consists of base power and contact force factor. Figure 30 Contour plot of response surface equation of the average stitch pull test that consists of base force and contact force factor.

35 Figure 31 Surface plot of response surface equation of the average stitch pull test that consists of contact force and base force factor.

Figure 32 Contour plot of response surface equation of the average stitch pull test that consists of base force and base power factor.

36 Figure 33 Surface plot of response surface equation of the average stitch pull test that consists of base power and base force factor. From the analysis of response surface equation of average stitch pull test, found that the equation is in maximum form, which equation gives only one optimum point that gives the maximum response while other points from other levels of factors cannot give the optimum. The optimum average stitch pull test level is 28.0764 grams-force, which is given from setting level of factors as the following, - Contact force at 218 grams-force - Base power at 86 DAC - Base force at 268 grams-force After we have got the proper level of control factors that give the optimum level of average stitch pull test, we would have confirmed the result of the analysis by performing the experiment on these proper levels of bond parameters and observing the process capabilities from samples 30 units, total 1,050 wires as the result is shown in figure 33.