jl – 5/4/01 engineering data analysis challenges in semiconductor manufacturing joe lebowitz...

JL – 5/4/01

Engineering Data Analysis Challenges Engineering Data Analysis Challenges in Semiconductor Manufacturingin Semiconductor Manufacturing

Joe Lebowitz

Director - Yield Engineering

Texas Instruments Inc.

Kilby Fab

5-4-01

(972) 995-2994 [email protected]

© 2001,Texas Instruments, Inc. - All Rights Reserved.

JL – 5/4/01

Our propensity to collect data is unparalleled, our ability to turn that data into useful information is…. catching up

One of our most important and plentiful products - but we don’t sell data!

We need to be able to use our data to maximize our product output – this demands an intuitive knowledge of how to interpret the analysis output.

More Data is Good, Good data is More!

EDA for Semiconductor MfgEDA for Semiconductor Mfg

JL – 5/4/01

EDA for Semiconductor MfgEDA for Semiconductor Mfg Faster data transfer and larger storage capacity is making huge

amounts of data available to engineers In today’s SC Mfg, new data streams are becoming available

Wafer and Die (chip) based data Process tool based data from Equipment Interfaces Advanced Process Control systems, Fault detection dataEvent data from PM’s, chemical batch changes, environmental conditions

Tens-hundreds of thousands of parameters collected on each lot/wafer. Much of this data is esoteric, but at any given moment a small percentage may be crucial!

How do we know what’s important? What isn’t? How do we minimize time to understanding?How do we mine all the data for maximum impact?How can we best visualize the data & translate it into meaningful action?

JL – 5/4/01

Engineering Data Analysis is a vital, integral part of Semiconductor development and manufacturing. The timely detection of problems, centering of complex process

interactions, and yield improvement hinges on efficient data analysis.

Most Engineers, faced with overwhelming amounts of data, will rely upon the analysis system to guide them to the pertinent information. This is becoming a more reliable thing to do as analysis systems become

more sophisticated.However, even the most sophisticated system, relying on rigorous

statistics, is often still far behind an experienced engineer’s intuitive analysis.

To the extent that Spotfire can enhance the experience of the engineers using it the tool will add greatly to the value of the data analysis efforts


JL – 5/4/01

Today’s highly data intensive semiconductor manufacturing environment requires specialization in the field of data analysis, yet much of the time we desire that our engineers do their own analysis.

Tools such as Spotfire can help ease the burden of doing good analysis quickly

The ability to center in on the important findings is key


JL – 5/4/01

WW38 Composite 6 lots WW39 Composite 25 lots

Product Engineering investigated a spatial pattern at Probe,

it was getting worse too.

EDA for Semiconductor MfgEDA for Semiconductor MfgEXAMPLEEXAMPLE

JL – 5/4/01

An analysis was run looking at differences in Parametrics in defined wfr areas. Many showed up as significant.

EDA for Semiconductor MfgEDA for Semiconductor MfgEXAMPLEEXAMPLE

JL – 5/4/01

The task at hand: Guide

our Engineers to the true solution as quickly as possible

The Analysis system showed many parameters as being statistically significant, yet only one really was. A lot of time went by while trying to determine which process to fix!

Time to fix = Lost Revenue!


JL – 5/4/01

Relationship “picked” by the analysis software - Regression line shows

continuous function. Should we even pick a regression through this shotgun

blast?!?

More intuitive Engineering “judged” relationship - Yield Cliff

Engineering Intuition (Experience)Engineering Intuition (Experience) Many Engineers, when faced with massive amounts of data, will

rely upon the analysis system to find a relationship. This may NOT always be the best way to look at the data!


JL – 5/4/01

Key Elements of a Data Analysis SystemKey Elements of a Data Analysis System

Fast & reliable, easy to learn and use

Provides both canned and customized analysis for the full spectrum of needs and users (Technician to Mgmt)

Capable of handling huge volumes of data from multiple sources

Works as an early warning system, monitoring for important changes

Data sourcing and formatting should be transparent to most users


JL – 5/4/01

Bitmap

Variable Definition

Limits Information

Device Definitions

Device Set-Up

Wfr Position

Defects

Split Information

Wafer Processing

In-Line Electrical

Parametric

Probe

Reliability

Final Test

Electrical Analysis

Process

Characterization

Physical FA

Customer Returns

Physical Analysis

Factory Automation

Tool State

Design Rules

Layout

APC controller


JL – 5/4/01

Must enhance the engineer’s knowledge of what the results mean, what actions need to be taken, and how to communicate all this.Easily interpreted results – visualization is key!

Reduce “false” signals which translate into lost revenue and opportunity

Engineering oversight on analysis resultsThe ability to interact with the data real time during the analysis

is key to keeping engineers involved in the analysis – this is a core strength of Spotfire

Key Elements of a Data Analysis SystemKey Elements of a Data Analysis SystemEDA for Semiconductor MfgEDA for Semiconductor Mfg

JL – 5/4/01

Key Elements of a Data Analysis SystemKey Elements of a Data Analysis SystemTo maximize effectiveness an analysis system should be

adaptive, learning what is and is not important and adjusting accordingly. Building an intelligent relational data base (i.e. last time the

PVt was 2mV off there was a problem with IMP02’s flood gun)

Must be able to comprehend how the Semiconductor Fab really runs Dedication schemes, process tweaks, non-centered to limits,

Bottleneck tools etc…


JL – 5/4/01

Provide high level capabilities for most engineers yet also function as an expert system for statisticians and Data Analysis experts.High level capabilities needed for routine, quick analysisMore detailed capabilities for deeper, searching kinds of

applications

The data analysis system must enhance the engineer’s experience and allow intuition to be used, pointing them in the right direction to find significant signals as fast as possible and communicating them effectively.

Key Elements of a Data Analysis SystemKey Elements of a Data Analysis SystemEDA for Semiconductor MfgEDA for Semiconductor Mfg

JL – 5/4/01

Imm

edia

cy a

nd

Sim

pli

city

Breadth and Depth

SAS

Web Tools

Mid Level Tools

Web

Rep

ortsWeb Reports

And Tools

Webtools

Mid Level Packages

High Level / User Customizable

Packages

• Few PE / PI / PDE• Analysis Specialists• Statisticians

Broad and Deep Analysis

• Large Scale Correlation• Cross Platform Analysis • Data Mining

• Advanced Training in Stats & Analysis methods

• Auto Reports• Browse able• Yields & Trends

• All Engineers• Product Lines• Management

• No special training

Point/Click Standards

Rapid Routine Analysis

• Process Integration• Product Engineering• Equip & Process Eng.

• Variable Correlation• Split Analysis• Simple Statistics

• General Technical Training

Complex and Specialty Analysis • Most PI / PDE/YE• Limited PE/EE• Analysis Specialists

• Complex Analysis• DD Test die• Rigorous Statistics

• More Specific Statistical & Analytical Training

Semiconductor Data Analysis Tool BoxSemiconductor Data Analysis Tool Box

JL – 5/4/01

Scatter Plot

LOG_DATE

005100510051

2336

4396

6054

7506

7896

8396

8716

12/16/00 11:59:26 PM 12/27/00 1/6/01 1/16/01 1/26/01 2/5/01 2/15/01

ILD2 loop Process Commonality:

ILD 2 Liner depo: DC49-2.

ILD2 HSQ depo: LT01C

Spotfire “Lot collision” curve

0343166 Lnom: 23.8%, Vcc 22.2%, Gross 27.8%

0343151 Lnom: 38.5%, Vcc 17.9%, Gross 5.1%

0348500 Lnom: 20.7%, Vcc 33.8%, Gross 16.7%

SRAM Fail vs ILD1 HSQ Edge Thickness

Thickness (A)

468

101214161820

35203540356035803600362036403660 3680

Old metal

New metal

SR

AM

fai

l rat

e (%

)


Early examples of Spotfire used in Semiconductor Mfg at TI that have helped visualize

problems and reach resolution

JL – 5/4/01

SummarySummaryToday’s Engineering Analysis systems are becoming

more proficient at providing meaningful resultsHowever, due to ever increasing volumes of data there is

more “noise” in the analysis due to random correlationsEngineers must be able to utilize their experience and

intuition in interpreting analysis results - “What makes sense?”

The unique ability to visualize the data and analysis results embedded in Spotfire allows it to be immediately useful in the SC environment


jl – 5/4/01 engineering data analysis challenges in semiconductor manufacturing joe lebowitz...

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