Adaptive Test Pattern Reordering for Diagnosis using k-Nearest Neighbors
| Autor: | Qicheng Huang, Chenlei Fang, R. D. Shawn Blanton |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 02 engineering and technology Integrated circuit computer.software_genre Chip 020202 computer hardware & architecture law.invention Test (assessment) k-nearest neighbors algorithm Reduction (complexity) Software law 0202 electrical engineering electronic engineering information engineering Computerized adaptive testing Data mining business computer Dynamic testing |
| Zdroj: | ITC-Asia |
| Popis: | Logic diagnosis is a software-based methodology to identify the behavior and location of defects in failing integrated circuits, which is an essential step in yield learning. However, accurate diagnosis requires a sufficient amount of failing data, which is in contradiction to the requirement of reducing test time and cost. In this work, a dynamic test pattern reordering method is proposed to “recommend” which test patterns should be applied for a given failing chip, with the goal of maximizing failing data while minimizing test time. Unlike prior work that uses population statistics from already tested chips, this method uses a machine learning technique, namely k-Nearest Neighbors. Experiments using three industrial chips demonstrate the efficacy of the proposed methodology; specifically, the recommended test pattern order led to a 35% reduction, on average, while maximizing the amount of failure data collected. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|