Semiconductor Wafer Spatial Pattern Classification With JSL

Don Kent – IMFlash Senior Product Engineer

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

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

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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

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Question:

What is NAND Flash Memory?

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…changed the way the world shares

information…

NAND Memory…

…changed the way the world captures images…

Then Now

Then Now

… and is now changing the way the world computes.

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300mm FLASH MEMORY WAFER Consist of hundreds of die

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Semiconductor Process Complexity [Step/Tool by Wafer]

• Many tool-step interactions

• What can go wrong?

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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

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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!

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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!

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• 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”

Spatial Pattern Software: High-Level Technical Summary

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.neighb

totadj

N

NNN

First 3 PC NN values

Feature Extraction: Moore Neighborhood and PCA

• 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

For each die the following ratio is calculated

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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

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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!

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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)

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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

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Automated Report Ex. –Finding Closest Pattern Matches

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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??

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