Test generation considering operating conditions and diagnosis issues for large volume yield improvement

Bharath Seshadri, Purdue University

Abstract

Testing and fault diagnosis are performed to detect and identify failures in manufactured integrated circuits. In this thesis, solutions to two important problems in testing and diagnosis are proposed. First, we address the problem of test generation considering operating condition variations. Variations in operating conditions cause path delays to change, resulting in different sets of critical paths at different operating conditions. We propose a method of identifying critical paths over a specified range of operating conditions. We also propose a method of N-detection test generation for transition faults, where each fault is tested through one or more longest paths considering a range of operating conditions. Second we address the problems of improving the speed of diagnosis and identifying systematic defects from a large amount of diagnosis data. Both these aspects of diagnosis can potentially enable rapid high volume diagnosis and help increase the rate of improving yield, or even the final yield itself. To speed up diagnostic fault simulation we propose using a combination of structural preprocessing and concurrent equivalence identification techniques. The structural method aids equivalence identification and together the two techniques speed up diagnosis. We propose an analysis method to identify systematic defects from large volume diagnosis data using design or process parameters. We also show a method to improve analysis sensitivity using multiple parameters. Finally, we suggest a procedure to order the multiple analysis steps to find strongly associated parameters.

Degree

Ph.D.

Advisors

Pomeranz, Purdue University.

Subject Area

Electrical engineering

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