1 Yield Analysis and Increasing Engineering Efficiency Spotfire Users Conference 10/15/2003 William Pressnall, Scott Lacey

2 Outline Introduction Semiconductor Background Yield Improvement System Example: Equipment Commonality Spotfire Usage Spofire Challenges

3 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.

4 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, Circuit Pattern Wafers – Flat discs of 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.

5 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.

6 Conceptual System Rapid yield learning requires Static, Dynamic and Interactive Capability Integrated User Friendly Tool Data Flow Static Reports Server Dynamic Reports Server/Client Interactive Data Mining Client

7 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

8 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.

9 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.

10 Equipment Commonality Looking at the trend of a specific yield bin of a device Select points with common characteristic Bad Good

11 Upon selection, query for all equipment data Run commonality statistics. Equipment Commonality

12 Select ranked statistics and view distribution. Does this make sense? Equipment Commonality

13 Spotfire Usage Single Site Multi-Site/Training Unique users executing queries for data. Spotfire 7.2 Deployment

14 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?

15 Spotfire Challenges – Data Access Wafer Fab In-line processing Electrical Test In-line Defect Electrical Failure Analysis Equipment Trace Excursion/ Lessons Learned Materials Distributed Data Sources Central Data Sources

16 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.

17 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.

18 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.

19 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. –Have just begun to implement the Analysis Builder and hope this can be effective.