Built-in test for VLSI: pseudorandom techniques
Built-in test for VLSI: pseudorandom techniques
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Low hardware overhead scan based 3-weight weighted random BIST
Proceedings of the IEEE International Test Conference 2001
Constructive Multi-Phase Test Point Insertion for Scan-Based BIST
Proceedings of the IEEE International Test Conference on Test and Design Validity
Proceedings of the IEEE International Test Conference on Discover the New World of Test and Design
Deterministic BIST with multiple scan chains
ITC '98 Proceedings of the 1998 IEEE International Test Conference
Virtual Scan Chains: A Means for Reducing Scan Length in Cores
VTS '00 Proceedings of the 18th IEEE VLSI Test Symposium
Pseudo Random Patterns Using Markov Sources for Scan BIST
ITC '02 Proceedings of the 2002 IEEE International Test Conference
Bit-fixing in pseudorandom sequences for scan BIST
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A BIST TPG for low power dissipation and high fault coverage
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An Optimized Seed-based Pseudo-random Test Pattern Generator: Theory and Implementation
Journal of Electronic Testing: Theory and Applications
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Recently, Markov sources were shown to be effective in designing pseudo-random test pattern generators with low area overhead for built-in self-test of scan designs. This paper presents a new test pattern generation scheme based on a Markov source and a partial bit-fixing technique. A new method is proposed for the computation of the state transition probabilities of the Markov source based on the statistics of a deterministic test set. This is enhanced by partial bit-fixing logic, which fixes a group of consecutive inputs to all-0 or all-1. Experimental results show that the proposed BIST scheme can achieve 100% fault coverage for large benchmark circuits with reduced hardware overhead and reduced pattern counts compared to the earlier method using Markov sources.