30 March 2017 Transforming information from silicon testing and design characterization into numerical data sets for yield learning
Author Affiliations +
Silicon testing results are regularly collected for a particular lot of wafers to study yield loss from test result diagnostics. Product engineers will analyze the diagnostic results and perform a number of physical failure analyses to detect systematic defects which cause yield loss for these sets of wafers in order to feedback the information to process engineers for process improvements. Most of time, the systematic defects that are detected are major issues or just one of the causes for the overall yield loss.

This paper will present a working flow for using design analysis techniques combined with diagnostic methods to systematically transform silicon testing information into physical layout information. A new set of the testing results are received from a new lot of wafers for the same product. We can then correlate all the diagnostic results from different periods of time to check which blocks or nets have been highlighted or stop occurring on the failure reports in order to monitor process changes which impact the yield. The design characteristic analysis flow is also implemented to find 1) the block connections on a design that have failed electrical test or 2) frequently used cells that been highlighted multiple times.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Yang, Yang Shen, Yifan Zhang, Jason Sweis, and Ya-Chieh Lai "Transforming information from silicon testing and design characterization into numerical data sets for yield learning", Proc. SPIE 10148, Design-Process-Technology Co-optimization for Manufacturability XI, 101480W (30 March 2017); doi: 10.1117/12.2259950; https://doi.org/10.1117/12.2259950

Back to Top