Transcript Preparing PPT for Presentation

Yield Analysis and Increasing
Engineering Efficiency
Spotfire Users Conference
10/15/2003
William Pressnall,
Scott Lacey
[email protected]
1
Outline
• Introduction
• Semiconductor Background
• Yield Improvement System
• Example: Equipment Commonality
• Spotfire Usage
• Spofire Challenges
[email protected]
2
Introduction
•
In the March of 2000 it was clear that the Data
Analysis system available to the engineers in
TI’s research and production facilities was not
competitive. The most basic analysis, such as
equipment commonality, was limited to
specific individuals with high level of
programming skills.
•
Development of an Engineering Data Analysis
(EDA) system became a top goal and a small
team was formed to: Design and deploy a
comprehensive integrated user friendly
analysis systems and methods for performing
state of the art engineering data analysis
system.
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3
Integrated Circuit Fabrication
• Yield is the the metric general used in the
IC industry which is the percentage of
functioning chips on a wafer.
Design – Photo Masks, Wafers – Flat discs of
Circuit Pattern
single crystal silicon
Materials – Gases,
Chemicals, Tooling
Wafer Fab – Factory that transforms the photo
masks, wafers ,and other materials to functioning
electrical devices meeting customer requirements.
Assembly/Test – Cuts the wafer into individual chips
and packages for mounting in the final customer
application.
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4
Yield Improvement
• Wafer Fabs employ a large engineering
community to improve the device yield by
changing the processing methods,
equipment configurations, design, and
materials.
• Yield improvement rate is driven by how
efficiently these engineers can, determine
the root cause, propose and evaluate
changes.
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5
Conceptual System
• Rapid yield learning requires Static,
Dynamic and Interactive Capability
Integrated User Friendly Tool
Data Flow
Static Reports
Dynamic Reports
Interactive
Data Mining
Server
Server/Client
Client
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6
Conceptual System
• Static Reports - Automatic, server
generated, Web based daily reports, Triggered
on events
• Dynamic Reports -User initiated queries
and filtering, Some server and some client
analysis
• Interactive Data-mining - Mostly client
side with server used as data transport, On the
fly re-calculation & re-query based on data
selection, Analyzed from libraries within the
application, or data can be exported to other
client analysis tools
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7
Tool Selection
• Existing system were selected for the Static
and Dynamic system.
• Interactive Data-mining – Spotfire: This was
the missing component that there was no
reasonable alternative. Spotfire was
selected because:
– Client interactive graphics and filtering capability
– Web based system and open architecture and
tools that enables “gluing” the other systems
together resulting in data flow between systems.
[email protected]
8
Equipment Commonality
• On of the most fundamental analysis is
Equipment Commonality – From a list
of “Good” lots and list of “Bad” lots,
which process tool has the greatest
probability of influencing this outcome?
• In this example a Yield trend line is the
starting point for selecting the “Good”
and “Bad” lot list.
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9
Equipment Commonality
• Looking at the
trend of a
specific yield
bin of a device
• Select points
with common
characteristic
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Bad
Good
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Equipment Commonality
• Upon selection,
query for all
equipment data
• Run
commonality
statistics.
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11
Equipment Commonality
• Select
ranked
statistics
and view
distribution.
• Does this
make sense?
[email protected]
12
Spotfire Usage
Spotfire 7.2
Deployment
Multi-Site/Training
Unique users
executing
queries for
data.
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Single Site
13
Spotfire Challenges – Criteria
• Data Access
– Navigation: How do if find what I want?
– Data Extraction response time: How how long does it take to
get it?
– Data Transformation: Is the data formatted to the analysis?
– Data Explanation: How does the data relate to the real world?
• Analysis
– Algorithm Selection: Is the best algorithm applied to find the
relationships in the data?
– Relationship Presentation: When found, is the relationship
clearly presented to the user?
• Results Communication
– Report Clarity: Is the finding reported in a format the general
audience will understand?
– Reproducibility: Are all the steps needed to make the same
report again documented?
[email protected]
14
Spotfire Challenges – Data Access
Central Data Sources
Distributed Data Sources
Electrical Test
In-line processing
Excursion/
Lessons
Learned
Wafer Fab
In-line Defect
Electrical
Failure
Analysis
Materials
Equipment
Trace
[email protected]
15
Spotfire Challenges – Data Sources
• TI’s data sources are locate at many different
geographic areas.
– Wafer Start in Freising, Germany
– Factory Processing data - Dallas, Texas
– Wafer level electrical testing – Freising
Germany
– Assembly and Final Testing – Taipei, Taiwan
• Experienced slow query response time
because Spotfire server is not located in the
same network zone as the databases.
[email protected]
16
Spotfire Challenges – Data Sources
• Data Documentation is big source of confusion
for the users. The current system on embedding
reference information in the workflow is efficient
for the user but a challenging task for workflow
developers.
– Workflow developer’s many times are not good
technical writers.
– Technical writers usually don’t use HTML and the there
is the potential to break the code it the editing is done
incorrectly.
[email protected]
17
Spotfire Challenges – Analysis
•
Statistical Analysis - For many
datasets, the most efficient method to
determining relationships is using
statistical algorithms. The base
Spotfire statistics are not adequate.
– Statistical packages such R and SAS
can produce the statistical results that
are confusing to the user. Need
statistical interpretation documentation in
Workflows.
[email protected]
18
Spotfire Challenges – Communication
•
Visualizations lacking:
–
–
–
–
•
Interactive Wafer Map
Box plot is Spotfire Application
Graphical Annotation feature
Grouped Bar Chart
Once a problem is found, the same
sequence needs to be repeatedly run as
the fix to the problem is implemented.
–
[email protected]
Have just begun to implement the Analysis
Builder and hope this can be effective.
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