1 / 5

ITRS Conference April 19 and 20 Stresa Italy 2004 ITRS Yield Enhancement (YE) Update

ITRS Conference April 19 and 20 Stresa Italy 2004 ITRS Yield Enhancement (YE) Update. Ines Thurner. Yield Enhancement TWG. Participants : Kevin Pate Intel, Chris Muller Purafil, Masahiko Ikeno Renesas, Dieter Rathei AMS, M.Retersdorf AMD, A. Neuber M + W Zander,

rune
Download Presentation

ITRS Conference April 19 and 20 Stresa Italy 2004 ITRS Yield Enhancement (YE) Update

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. ITRS Conference April 19 and 20 Stresa Italy 2004 ITRSYield Enhancement (YE) Update Ines Thurner

  2. Yield Enhancement TWG Participants : Kevin Pate Intel, Chris Muller Purafil, Masahiko Ikeno Renesas, Dieter Rathei AMS, M.Retersdorf AMD, A. Neuber M + W Zander, A. Nutsch Fraunhofer IISB , L. Pfitzner Fraunhofer IISB, Ines Thurner Infineon

  3. YE Difficult Challenges • High-Aspect-Ratio Inspection. • High-speed, cost-effective tools are needed to rapidly detect defects at 1/2 X ground rule (GR) associated with high-aspect-ratio contacts, vias, and trenches and especially defects near or at the bottoms of these features. • Design for Manufacture & Test (DFM & DFT) • IC designs must be optimized for a given process capability and must be testable and diagnosable • Correlation of Impurity Level to Yield. • Data, test structures and methods are needed for correlating process fluid contamination types and levels to yield and determine required control limits • Data Management and Test Structures for Rapid Yield Learning. • Automated, intelligent structures, analysis and reduction algorithms that correlate facility, design, process, test, and work-in-process (WIP) data must be developed to enable the rapid root-cause analysis of yield-limiting conditions.

  4. YE Difficult Challenges (continue) • Yield Models • Random, systematic, parametric, and memory redundancy models must be developed and validated to correlate process-induced defects (PID), particle counts per wafer pass (PWP), and in-situ tool/process measurements to yield. • Systematic Mechanisms Limited Yield (SMLY) • Understanding SMLY is mandatory for achieving historic yield ramps in the future. • Non-visual Defect Detection • In-line and end-of-line tools and techniques are needed to detect non-visual defects.

  5. YE – focus 2004/2005 • Relative importance of different contaminants to wafer yield • Continue validation of contamination targets thru benchmarking • New defect budget survey to correct or validate defect budgets for process tools and improve yield model • Integrate test structure in yield learning subtopic • Wafer inspection of backside, bevel, edge, nanotopographie and geometry

More Related