Name
Abstract | ||
|---|---|---|
As electronic products become feature-rich, the amount of manufacturing testing required grows rapidly. This is further compounded because products made with components from several vendors, and in factories distributed all over the world, have similar tests repeated at various stages. While testing adds great value, it can also substantially increase manufacturing costs. In this paper we present a computational scheme that allows test strategists to investigate, and optimize, the nonintuitive trade-offs between test cost and product quality along the entire manufacturing chain. A method for constructing a mathematical model of very general test chains is presented that allows for the complete representation of realistic test and repair operations, down to the subtest level, including tests and repairs that have limited accuracy. A test-feature vector is introduced to account for test coverage. In addition, the notion of good and bad is introduced for every feature to allow the quantizing of quality. (c) 2007 Alcatel-Lucent. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1002/bltj.20224 | Bell Labs Technical Journal |
Keywords | Field | DocType |
mathematical model,economic model | Code coverage,Economic model,Computer science,Manufacturing testing,Quantization (signal processing),Mathematical model,Test strategy,Reliability engineering | Journal |
Volume | Issue | ISSN |
12 | 1 | 1089-7089 |
Citations | PageRank | References |
2 | 0.63 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric S. Fisher | 1 | 4 | 1.15 |
| Steven Fortune | 2 | 125 | 12.86 |
| Martin K. Gladstein | 3 | 4 | 1.15 |
| suresh goyal | 4 | 120 | 13.77 |
| William B. Lyons | 5 | 8 | 2.51 |
| James H. Mosher Jr. | 6 | 4 | 1.15 |
| Gordon T. Wilfong | 7 | 907 | 171.50 |