Identification of Rogue Tools and Process Stage Drift by using JMP Software Visualization and Analytical Techniques

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Visualization and Analytical Techniques Jim Nelson

1 September 16 th, 2010 Identification of Rogue Tools and Process Stage Drift by using JMP Software Visualization and Analytical Techniques Jim Nelson Engineering Data Specialist, Freescale Semiconductor, Inc.

2 Abstract The making of integrated circuits (computer chips) takes hundreds of very exacting series of steps. Hundreds of different pieces of equipment are used within those steps. Process drift and yield drops are a feared fact of life in the semiconductor industry. Identification of which process steps and/or tool or combination of tools that contributed to the process drift or yield drop is a time consuming and complex process taking up considerable amount of the engineers time within Freescale. A combination of JMP software delivered analytical tools, Freescale developed statistical methodologies and Freescale developed analytical and visualization JMP scripts are being used to solve these issues. This paper describes the use of partitioning, a Freescale developed methodology, Step Origin of a Drift Analysis(SODA), and two Freescale developed JMP software scripts are discussed in this paper. The scripts allow for ordering of the charts and graphs based upon statistical evaluation along with the ability to move from chart to chart very quickly. This methodology allows for a very positive combination of analytical guidance coupled with tools that provide the engineers with visual evidence to match with their knowledge and experience in making decisions on what needs to be done to bring the processes back into alignment. 2

3 Semiconductor Manufacturing Overview 3

4 Overview of a Process Step Each of the steps in the manufacturing process typically uses a tool with a specific functionality to accomplish the process within that step. The exact tool used can be different for each lot or possibly wafer, if more than 1 of the specific tool are available. Multiple tools to choose from in each step???? 4

5 Overview of a Process Step With hundreds of different tools used within the hundreds of different steps, one can see how a single tool or a combination of tools that drift off from their optimum settings can impact the quality of the product. 5

6 Overview of a Process Step Each of the steps in the manufacturing process will have inputs that affect the product no matter what tool is used. Consumables such as chemicals. New batch Age of the consumable Changes made in the process prior to a step that result in a parametric drift at a later stage Changes in global distribution systems. 6

7 Overview of a Process Step Such changes in a process are typically seen as a drift in the measured parametric values. Since most of the parametric measurements are not made until the end of all of the process steps, identifying the root cause of the drift can be a challenge. 7

Rogue Tool Identification If the Yield or Parametric excursion is truly caused by a single rogue tool, identification of that tool may simply rely on being able to access the tool history for the

8 Rogue Tool Identification If the Yield or Parametric excursion is truly caused by a single rogue tool, identification of that tool may simply rely on being able to access the tool history for the poorly performing lots and running a One-way ANOVA between the tools used at the various steps. To this end, Freescale implemented a very large Teradata Data Warehouse to give global access to all of the test data for our products. With the data being accessible, and JMP Software, running the several hundred One-way ANOVAs became a simple task. Database Extraction Tool Analysis 8

9 Rogue Tool Identification What becomes a problem now, is finding the tool(s) with significant differences. While scrolling through hundreds of ANOVAs isn t difficult, it is time consuming. To make this process more efficient, an FSL version of Fit Y by X script was written to order the output based upon the significance of the ANOVAs. 9

10 Rogue Tool Identification Table of Results, Ordered By Significance All ANOVAs are Displayed in Order of Significance, 10

11 Rogue Tool Identification While the ordered Fit Y by X permits identification of rogue tools in a fairly straight forward manner, if the problem is really a process drift issue, this technique will not pick it up. To deal with the identification of a drift and/or rogue tool possibility in one analysis, a JMP script called LEH Correlation was developed. (Lot Equipment History Correlation) This script combines both a statistical analysis of the differences between tools at each of the process steps, with a visual analysis of how the yield or parametric value has drifted across time. 11

Order Window allows the engineer to see the different steps of the

12 Rogue Tool Identification with Drift Analysis This step appears to have a tool that has fail rates significantly higher than the other tools The Order Window allows the engineer to see the different steps of the process in the order of the differences between the equipment at each step 12

engineer selects the step they want to review, the charts in the

13 Rogue Tool Identification with Drift Analysis The charts show a possible drift with fail rates trending up over time When the engineer selects the step they want to review, the charts in the LEH Correlation window are updated with the data from that step 13

14 Rogue Tool Identification with Drift Analysis The LEH Correlation script has proven to be a very powerful tool for the identification of Rogue Tools and Drift Analysis. The engineers have accepted the tool very quickly. It provides a very tangible display with a statistical analysis they are comfortable with. The script does attempt to quantify the Rogue Tool analysis by using statistical comparisons (ANOVA and Tukey-Kramer), the drift analysis is strictly a visual evaluation. It is a script, however, who s effectiveness is itself, is highly correlated with the experience of the engineer. The analysis is really a bivariate analysis, repeated hundreds of times, with the engineer playing the role of the multivariate engine, putting sense into the equation across all of the separate analyses. Junior level engineers have been known to look at the same output, and not see the drift or the rogue tool. 14

15 Rogue Tool Identification with Drift Analysis Two methodologies have been explored within Freescale in an attempt to improve on the experience based analyses. Partitioning to identify Rogue Tools Step Origin of a Drift Analysis (SODA) 15

Partitioning to Identify Rogue Tools The ordered Fit Y by X and the LEH Correlation are both effective methods for finding the tool that is the root cause of an increase in failures.

16 Partitioning to Identify Rogue Tools The ordered Fit Y by X and the LEH Correlation are both effective methods for finding the tool that is the root cause of an increase in failures. Finding the root cause when it is coming from an interaction between tools is very difficult to find with such tools. Using Partitioning (Decision Tree, CART, MARS, etc.), can identify not only single predictor tools, but tools that in combination are resulting in the poor performance of the product. Source: Modeling and Multivariate Methods, JMP Software Documentation, SAS Inst. Inc.,

table Columns are tool based with a cell value based upon

17 Partitioning to Identify Rogue Tools A script was written to transform the LEH step based data table into a tool based data table Columns are tool based with a cell value based upon whether or not the lot was run on the tool Columns are step based 17

18 Partitioning to Identify Rogue Tools 1. The displayed Decision Tree shows two different interactions that are contributing to low yielding product

19 Partitioning to Identify Rogue Tools While partitioning is a strong tool for finding Rogue Tool(s), the acceptance of the methodology by the engineers remains a challenge. 19

20 Step Origin of a Drift Analysis (SODA) The SODA application was developed at the Freescale facility in Toulouse, France.* Originally it was implemented in SAS, but has been translated into a JMP script. SODA identifies process drift *Yield Improvement Using Statistical Analysis of Process Dates, François Bergeret, Caroline Le Gall, IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 16, NO. 3, AUGUST

21 SODA methodology is based on lot mixing during fabrication. Each graph represents a process stage from the same sample of lots. The response variable, for example a reject rate at an electrical test, is drawn as a function of the manufacturing order of the lots at the process stage. It is likely that stage described [Fig. 1(a)] is the defective stage because a sudden deterioration has occurred: before this deterioration, all the lots were good, and after, all the lots are bad regardless of the tool used in processing the lots. At the other stage [Fig. 1(b)], bad lots are not processed consecutively. In this case, it is likely that this stage is not the defective stage. Therefore, even if the 10 values of the response variable are the same in the two graphs, the defective stage can be identified by the lot mixing. How does SODA work? 21

22 How does SODA work? The idea is to detect the stage where the transition between good and bad lots is the most important. According to the defective stage definition, a curve can more easily be fit to the defective stage than to the non-defective stage because at a nondefective stage, there is more variation than at the defective stage. Using a goodness of fit criterion, the defective stage should be identified as the stage with the best fitted curve. Smoothing cubic spline approach is used to fit the stages. The cubic spline method uses a set of third-degree polynomials spliced together such that the resulting curve is continuous and smooth at the splices (knot points). The number of knots is a tuning parameter in the spline formula. This number has a strong impact on results. So the choice is very important. 22

23 Choice of the number of knots : Because natural lot mixing between process stages is weak, graph of neighboring stages are very similar. So for a number of knots, when we assess the goodness of fit for each process stages, neighboring stages should have similar results. This is true only when the drift observed on the response variable is coming from a faulty process stage. We use this observation as a criteria to choose the best number of knots. In a first time, we perform the analysis for a range of number of knots (5 to 20). For each number of knots, the ranking of stages is performed according the goodness of fit. The best number of knots is the one that makes the ranking of the best fitted stages the most homogeneous as possible, i.e. process stages are the closest as possible within the process flow. 23

24 Real Life Example The response variable is the reject at an electrical test (Test 30), it is called P42030 in the following. In a first time, we have to choose the best number of knots, so the minimum of knots is fixed at 5 and the maximum at

25 Real Life Example This is important to check that not too many lots are removed from the analysis. List of process stages removed from the analysis because of not enough lots passed through. Chronology of process stages kept for the analysis. May be useful to have a look at the process flow. Results for each number of knots. Choice of the best number of knots 25

26 Real Life Example The most the criteria moydelta is low, the most homogeneous the ranking is. For the following, we will decide to choose 11 as the number of knots. 26

Real Life Example RMSE is the goodness of fit criteria. It stands for Root Mean Square of Error. The most the smoothing is well adjusted, the least the RMSE is.

27 Real Life Example RMSE is the goodness of fit criteria. It stands for Root Mean Square of Error. The most the smoothing is well adjusted, the least the RMSE is. The ranking shows the 10 most suspicious stages. Results are quite accurate because the RKs (ranking within the process flow) are very close, so the ranking is homogeneous according the process neighborhood. Last stages of the ranking is the least suspicious stages. It is given just to compare the RK and check that the least suspicious stages is not close to the most suspicious. It is a criteria to check results accuracy. 27

It is necessary to compare the most suspicious stages with the least suspicious in

28 Real Life Example Graphs of process stages within the ranking are displayed in the Plots item. It is necessary to compare the most suspicious stages with the least suspicious in order to assess the accuracy of results. The confidence level in the results is very low if graphs of the most and the least suspicious are very similar. 28

29 Conclusions While JMP Software provides a strong analytical platform for data visualization and statistical analysis, it is the ability to customize the software that brings out it s power and makes it acceptable to engineers in the semiconductor world. Even with the customization, moving the engineers into new paradigms is a slow process. The JMP environment allows for a consistent, user friendly environment that can introduce change at a pace that allows for a steady adoption of new analytical methods. 29

Major Product Crisis: 8D Resolution Process With JMP Véronique Audran-Esturillo, Jerome Bonnouvrier, Franck Richard ULIS

Major Product Crisis: 8D Resolution Process With JMP Véronique Audran-Esturillo, Jerome Bonnouvrier, Franck Richard ULIS Abstract Methods Conclusions In the past, faced a major crisis which impacted the functionality of the big-runner products. The application JMP was successfully used during the main steps of crisis resolution,