Optimal interconnect diagnosis of wiring networks
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Sweeping Line Approach to Interconnect Testing
IEEE Transactions on Computers
Maximal Diagnosis for Wiring Networks
Proceedings of the IEEE International Test Conference on Test: Faster, Better, Sooner
Layout-driven detection of bridge faults in interconnects
DFT '96 Proceedings of the 1996 Workshop on Defect and Fault-Tolerance in VLSI Systems
DAC '82 Proceedings of the 19th Design Automation Conference
A New Diagnosis Approach for Short Faults in Interconnects
FTCS '95 Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing
Diagnosis of interconnects and FPICs using a structured walking-1 approach
VTS '95 Proceedings of the 13th IEEE VLSI Test Symposium
IEEE standard 1500 compatible interconnect diagnosis for delay and crosstalk faults
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
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This paper presents a new approach for diagnosing shorts in interconnects in which the adjacencies between nets are known. This structural approach exploits different graph coloring techniques to generate a test set with no aliasing and confounding, i.e., full diagnosis (detection and location) is accomplished. Initially, a simple coloring approach based on a greedy condition of the adjacency graph is proposed for fault detection. Then, the conditions for aliasing and confounding are analyzed with respect to the sizes of the possible shorts. These results are used to generate new colors using a process called color mixing. Color mixing guarantees that additional tests, required in order to avoid aliasing/confounding, will use appropriate codes. The characteristics of unbalanced/balanced codes for encoding the colors in the vector-generation process of interconnect diagnosis are discussed and are proved to yield full diagnosis using a novel method. An algorithm for full diagnosis is then presented; this algorithm has an execution complexity of O(max{N2, N×D3}) where N is the number of nets and D is the maximum degree of the nodes in the adjacency graph. Simulation results show that the proposed approach requires a smaller number of test vectors than previous approaches.