Semiconductor Wafer Spatial Pattern Classification With JSL. Don Kent IMFlash Senior Product Engineer

Transcription

1 Semiconductor Wafer Spatial Pattern Classification With JSL Don Kent IMFlash Senior Product Engineer

2 April 4, 2005 Via Della Conciliazione, Pope Benedict inauguration Slide - 2

3 March 13, 2013 Via Della Conciliazione, Pope Francis inauguration Slide - 3

4 IMFlash High-Tech Semiconductor joint venture between Intel and Micron Formed in 2006 Multi Billion investment Located in Lehi, Utah ~1600 Employees Manufactures Non-Volatile Memory products Slide - 4

5 Question: What is NAND Flash Memory? Slide Slide - 5-5

6 NAND Memory changed the way the world shares information Then Now changed the way the world captures images Then Now and is now changing the way the world computes. Slide - 6

7 300mm FLASH MEMORY WAFER Consist of hundreds of die Slide - 7

8 Semiconductor Process Complexity [Step/Tool by Wafer] Many tool-step interactions What can go wrong? Slide - 8

9 Spatial Pattern Classification: Motivation When something does go wrong: how to find root cause ASAP Many yield detection and improvement opportunities (i.e. Yield Tail wafer below) are associated with wafers that have distinct spatial signatures Fail patterns often provide a big clue if that data can be obtained / extracted Yield and Reliability Opportunities Slide - 9

10 Spatial Pattern Classification: Introduction Problem -how to quickly classify hundreds (thousands?) of wafers by their spatial patterns? Manual methods do not scale -need more eyes To perform Wafer Classification based on spatial signatures: Need Wafer Pattern Extraction, Classification and Visualization by combining high-dimensional analysis techniques (PCA for feature extraction) with unsupervised learning (Clustering) in a dynamic (JSL) wafer visualization (GraphBuilder) application (Addin) All can be done through JMP! Slide - 10

11 Many Benefits of the JMP Solution Spatial Pattern Analysis Software Benefits Include Accessibility to many engineers/technicians updated through a JMP Addin Interactive what if analysis Creation of a spatial metric More Accurate eliminate manual scoring and false positive/negatives Quicker Root Cause Detection Statistical measures of group similarity and dispersion Easy to create Pattern Libraries Capability of applying distance measures Once you have the pattern vector created possibilities expand Calculate distances to known wafers or Pattern Centroids With slight tweaks it is possible to completely automate Daily reports Victory! Slide - 11

12 Spatial Pattern Software: High-Level Technical Summary Feature Extraction Analyst choose character or numeric response Script -create Bin pareto (Char) or distribution Analyst decide on engineering options Script -calculates the Moore Neighborhood for each die (NN up to 8 Nearest Neighbors) Script -transform the NN data to a row vector for each wafer; perform a Principal Component Analysis; extract the top PC s(feature Extraction) Clustering Script perform (clustering) combined with a visualization of the 3D scatterplot to visualize the Wafer Clusters Analyst -dynamically adjust the clusters as ascribed by the analyst this will be unique for each data set Visualization Script -Map clusters (overlay wafer maps) and provide a Cluster Summary Slide - 12

13 Feature Extraction: Moore Neighborhood and PCA For each die the following ratio is calculated NN N N totadj neighb. where N-totadj is the total number of failing neighbors dies while N-neighb is the number of neighbors considered (8 for Moore neighbors). NN range between 0 and 1. At the edge of wafer the number of neighbors is less than 8 and then we need to take care of this. This is equivalent to applying an average filter (others from image analysis include gaussian) Once the variable NN has been determined for all die belonging to the same wfs, the first 3 Principal Components (on the transposed wafer vector) are calculated Those variables will be the ones clustered on Principal components analysis has been used for high dimension feature extraction for many years the goal being to represent the high dimensional space with fewer independent variables NN values First 3 PC Slide - 13

14 De-noising Denoising will remove single/double/triple die Used to eliminate single(multiple) die that have no adjacency This can improve the output if you are looking for clusters Denoising is done as part of the NN calculation Slide - 14

15 Design Features Driven by the Customer Listen, Listen, and Listen to your customers! Critical to understand their needs and how they will use the software Work with them to understand their process flow Know the target audience Do they know about clustering? Understand how overlay maps work? Documentation/Help support?? You are too close to the issue/data need to step back and listen/observe Additional Features in Spatial Pattern Classification Script Sandbox so multiple instances can co-exist (use Namespaces!) Numeric response capability (tips following) Capability to save clusters as specific names: Cluster Library Yield engineering likes to name things human readable names not cluster 12 Ability to create wafermaps for each cluster with a summary report by lot/wafer Help and Documentation! Slide - 15

16 Tuning the Data to give the Best Results Most common issues Not all die represented in a wafer will crash PCA Solution impute the missing die (JMP 12 or your own custom script) Try changing Clustering Algorithm (Advanced Clustering) Self Organizing Maps Normal Mixtures** Really like the Outlier cluster -cleanup Tweaking Data Winsorizing Max/Min limits By applying a limit to a die you can see interesting patterns (reticle shading ) Recoding data For numeric responses, I generally like to standardize the overall data set this has the advantage of making the output maps easy to adjust the scale Recoding data to give more weight to patterns you are interested in (Contrast Knob) Slide - 16

17 What s Next Automated Spatial Pattern Classification Goal Create a report for every Low Yielding wafer that determines distance to other wafers in the low-yield DB and return closest wafers (by their pattern) Idea here is that other wafers in the pattern DB have already been sourced If the wafer in question finds a match then perhaps we already know the cause This is similar to classification but now we have a known wafer and need to use distance measures to find similar wafer patterns Algorithm would be similar to interactive tool Wafer Pattern Extraction Neighborhood analysis Distance Analysis Use a variety of distance measures against the raw wafer vectors Pearson Correlation, Cosine Correlation, Bray-Curtis Dissimilarity, Euclidean Visualization Report all through JMP! Clustering based on distance measures Wafer maps for visualization Slide - 17

18 Automated Report Ex. Finding Closest Pattern Matches Slide - 18

19 Acknowledgements / Questions A big thanks to my initial collaborator Felice Russo Collaborated on initial software creation/design Led the initial evaluation team Helped with documentation IMFlash Data / Yield Enhancement Team Greg Christensen Justin Harnish Mark Rasmussen Landon Jensen Vinod Anumareddy Questions?? Slide - 19

20 2014 IM Flash Technologies, LLC. All rights reserved. Products are warranted only to meet the applicable production data sheet specifications. Information, products and/or specifications are subject to change without notice. All information is provided on an AS IS basis without warranties of any kind. Dates are estimates only. Drawings may not be to scale. All trademarks are the property of their respective owners.