Name
Abstract | ||
|---|---|---|
Power supply noise induced IR drop can cause transition delay faults (TDF) clustered in a small region. However, traditional diagnosis technique cannot handle clustered multiple TDF very well. This paper proposes a diagnosis tool for clustered multiple TDF. Star tracing for TDF is applied to find possible suspects. To tolerate fault masking and fault reinforcement effects, we propose an approximate covering heuristic to find a group of suspects. During approximate covering, we extract important suspects which are likely to be true suspects. We assume many suspects physically cluster around true suspects so our technique prunes suspects based on this assumption. We use correlation coefficient to determine the optimal number of clusters (Optimal NC) so we can apply the K-means algorithm to group suspects. Finally, we prune the least possible cluster but keep important suspects. Simulation on benchmark circuits shows that average accuracy of our tool (0.80) is much better than that of a commercial tool (0.47). Average resolution of our tool (0.35) is also better than that of the commercial tool (0.23). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ITC-Asia51099.2020.00021 | 2020 IEEE International Test Conference in Asia (ITC-Asia) |
Keywords | DocType | ISBN |
clustered multiple faults,multiple faults diagnosis,transition delay faults diagnosis | Conference | 978-1-7281-8944-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Shen You | 1 | 0 | 0.34 |
| Chih-Yan Liu | 2 | 0 | 0.34 |
| Mu-Ting Wu | 3 | 0 | 1.35 |
| Po-Wei Chen | 4 | 0 | 0.68 |
| James Chien-Mo Li | 5 | 187 | 27.16 |