Control Charts For Semiconductor Defect Management

January 20, 2009 Copyright Stuart L. Riley 1

Control Charts For Semiconductor Defect Management

Stuart L. [email protected]

Member American Society for Quality


Copyright Statement

Original work by Stuart L. Riley: Copyright 2010

Rights reserved.

This document may be downloaded for personal use.

Users are forbidden to reproduce, republish, redistribute, or resell any materials from this document as their original work.

All references to this document, any quotation, or figures should be made to the author.

Questions or comments can be addressed to Stuart L. Riley, at [email protected] or [email protected]


Introduction

• We will briefly explore a few ways to create control charts based on in-line defect inspection data

• Several examples are shown using real defect data


Types of Control Charts For Defect Data• Individuals: XmR Charts

– Continuous data – defect density, defect-limited yield (dly)1

– Used when data cannot be grouped into rational subgroups– The process distribution may be very skewed or bounded (non-normal)– May be applied to data with outliers– Each observation represents the process at a given point in time– The data is time-ordered and entered in the sequence which it was generated

• C-Chart– Discrete data - typically used for defect count data2

– Can have problems with outlier data– Plot the number of defectives (per batch, per day, per machine, etc.)– Control limits in this chart are computed based on the Poisson distribution

1: http://www.valaddsoft.com/Documents/Semiconductor-Defect-Management---Separating-the-Vital-Few-From-the-Trival-Many.pdf

2: http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc331.htm


Types of Control Charts For Defect Data

• Run Charts– Sometimes used when there is insufficient data to apply SPC charts– Lacks the benefits of control charts– Upward and downward trends, cycles, and large aberrations may be spotted – Can be used to track improvements that have been put into place– Average centerline can be added to see movement about the average– The data is time-ordered and entered in the sequence which it was generated– Try not to depend on these types of charts, unless you really need them


Selection Criteria – Which Chart to Use

These are the only charts that can be used for defect control.

Source: http://peltiertech.com/WordPress/types-of-control-charts/


Individuals (XmR) Chart Control Limits

• Mean range is the common method for finding limits for XmR• Median range can be used for charts with large variation in the

range (outliers)

12

1

m

i ii

x xMeanof the Range MR

m

−=

−= =

−

∑

1

m

ii

xX

m==∑

Medianof the range MR=

2.660XUCL X MR= + ×

2.660XLCL X MR= − ×

3.145XUCL X MR= + ×

3.145XLCL X MR= − ×

3.268RUCL MR= × 3.865RUCL MR= ×


C-Chart Control Limits

Total number of defectscTotal number in sample

=

3cUCL c c= + ×

3cUCL c c= − ×


C-Chart Defect Counts Example 1

The UCL for this chart looks reasonable.

This chart is not too noisy.


XmR Chart Defect Density Example 1: Avg R

The UCLs for these charts look a bit too high.


XmR Chart Defect Density Example 1: Median R

The UCLs for these charts look better.


XmR Chart DLY Example 1: Avg R

The UCLs on these charts look reasonable.

The same defect data shown in the previous charts has been converted to defect-limited yields (DLY)*.

The data is not as noisy as with the defect count or density charts.* http://www.valaddsoft.com/Documents/Semiconductor-Defect-Management---Separating-the-Vital-Few-From-the-Trival-Many.pdf


XmR Chart DLY Example 1: Median R

The UCLs on these charts look reasonable.




C-Chart Defect Count Example 2

The UCL for this chart looks too low.

This is an extremely noisy chart.


XmR Chart Defect Density Example 2: Avg R

These charts look too noisy to set a reasonable limit.


XmR Chart Defect Density Example 2: Median R

These charts look too noisy to set a reasonable limit.


XmR Chart DLY Example 2: Avg R

These limits look reasonable.




XmR Chart DLY Example 2: Median R

These limits may be a bit too aggressive, but they are not too bad.




Summary

• XmR charts are the typically the best choice for defect control charts

• C-Charts rarely work well for noisy data, so they should be avoided

• If the data is too noisy, you can always use a run chart, but you will loose the benefits of control charts

• The best solution is to use defect-limited yield* control charts– It is the appropriate metric to track the yield impact of defects on wafers– It tends to smooth out the noise to focus attention to real issues– It makes setting up defect control charts much easier

* http://www.valaddsoft.com/Documents/Semiconductor-Defect-Management---Separating-the-Vital-Few-From-the-Trival-Many.pdf

Control Charts For Semiconductor Defect Management

Business

Transcript of Control Charts For Semiconductor Defect Management