Parallel Test Pattern Generation Research Articles

P4-TPG: Accelerating Deterministic Parallel Test Pattern Generation by Preemptive, Proactive, and Preventive Schedulings

According to the prior research, a deterministic parallel test pattern generation (TPG) engine was realized and generated the same test pattern set the serial automatic test pattern generation does during acceleration. However, for retaining the determinism, tremendous idle time is observed when different tasks (either dependent or independent) were synchronized among threads. Therefore, a new deterministic parallel TPG engine called P4-TPG is developed and incorporates preemptive, proactive, and preventive schedulings to further save/reuse the idle time during acceleration. In P4-TPG, preemptive scheduling first modifies the thread flow and brings forward, as many latter tasks as possible, to the idle time. Next, proactive scheduling inserts prospective TPG tasks of unprocessed faults to the remaining idle time and increases the overall utilization of threads. Last, preventive scheduling dynamically skips faults incompatible with the working pattern per thread and shortens the fault list during fault compaction. The experimental results show that P4-TPG not only generates the same test pattern set as the serial TPG does but also achieves averagely $10.36\times $ speedups, is 96.6% better than the prior research, using 12 threads on 18 benchmark circuits.

A Parallel Test Pattern Generation Algorithm to Meet Multiple Quality Objectives

This paper proposes a bit-level parallel ATPG algorithm (SWK) that generates multiple test patterns at a time. This algorithm converts decisions into bitwise logic operation so that W (CPU word size) test patterns are searched independently. Multiple objectives for different quality metrics can therefore be achieved in a single test generation process. Experimental results on ISCAS'89 and IWLS'05 benchmark circuits show that SWK test sets are better in many quality metrics than traditional 50-detect test sets, while the length of the former is shorter. Also, patterns selected from large N-detect pattern pool cannot achieve the same or higher quality than patterns generated by SWK.