Name
Abstract | ||
|---|---|---|
Conventional fault models define their faulty behavior at the IO ports of standard cells with simple rules of fault activation and fault propagation. However, there still exist some defects inside a cell (intra-cell) or between two cells (dual-cell) that cannot be effectively detected by the test patterns of conventional fault models and hence become a source of DPPM. In order to further increase the defect coverage, many research works have been conducted to study the fault models resulting from different types of intra-cell and dual-cell defects, by SPICE-simulating each targeted defect with its equivalent circuit-level defect model. However, it was considered computationally infeasible to simulate every possible defective scenario for a cell library and obtain a complete set of cell-level fault models. In this paper, we present a new dual-cell-aware (DCA) framework based on examining the layout of two adjacent cells (i.e., a dual cell) to identify potential defects, where time-consuming RC extraction can be avoided and the runtime for SPICE simulation can be reduced. Experimental results and silicon data on a SoC product show that the proposed DCA framework can not only save runtime significantly but also maintain the promising efficacy of DCA tests for the objective of lowering DPPM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VTS.2019.8758646 | 2019 IEEE 37th VLSI Test Symposium (VTS) |
Keywords | DocType | ISSN |
dual-cell-aware tests,standard cells,fault activation,fault propagation,test patterns,defect coverage,dual-cell defects,equivalent circuit-level defect model,cell library,cell-level fault models,dual-cell-aware framework,adjacent cells,SPICE simulation,DCA tests | Conference | 1093-0167 |
ISBN | Citations | PageRank |
978-1-7281-1171-1 | 0 | 0.34 |
References | Authors | |
6 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tse-Wei Wu | 1 | 0 | 0.34 |
| Dong-Zhen Lee | 2 | 0 | 0.34 |
| Yu-Hao Huang | 3 | 1 | 0.70 |
| Mango C.-T. Chao | 4 | 48 | 7.38 |
| Kai-Chiang Wu | 5 | 113 | 13.98 |
| Shu-Yi Kao | 6 | 2 | 1.40 |
| Ying-Yen Chen | 7 | 2 | 1.74 |
| Po-Lin Chen | 8 | 1 | 0.71 |
| Mason Chern | 9 | 2 | 1.40 |
| Jih-Nung Lee | 10 | 14 | 3.13 |