This research develops yield and reliability models for fault-tolerant semiconductor integrated circuits and develops optimization algorithms that can be directly applied to these models. Since defects cause failures in microelectronics systems, accurate yield and reliability models considering these defects as well as optimization techniques determining efficient defect-tolerant schemes are essential in semiconductor manufacturing and nanomanufacturing to ensure manufacturability and productivity. The defect-based yield model considers various types of failures, fault-tolerant schemes such as hierarchical redundancy and error correcting code, and burn-in effects, simultaneously. The reliability model counts on carry-over single-cell failures accompanied by the failure rate of the semiconductor integrated circuits under the assumption of an error correcting code policy. The redundancy allocation problem, which seeks to find an optimal allocation of redundancy that maximizes system reliability, is one of the representative problems in reliability optimization. The problem is typically formulated as a nonconvex integer nonlinear programming problem that is nonseparable and coherent. Two iterative heuristics, tree and scanning heuristics, and variants are studied to obtain local optima and a branch-and-bound algorithm is proposed to find the global optimum for redundancy allocation problems. The proposed algorithms engage a multiple-search paths strategy to accelerate efficiency. Experimental results of these algorithms indicate that they are superior to the existing algorithms in terms of computation time and solution quality. An example of memory semiconductor integrated circuits is presented to show the applicability of both the yield and reliability models and the optimization algorithms to fault-tolerant semiconductor integrated circuits.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2777 |
| Date | 01 November 2005 |
| Creators | Ha, Chunghun |
| Contributors | Kuo, Way |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 862508 bytes, electronic, application/pdf, born digital |
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