Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Process control monitoring (PCM) data provide information that is used to track abnormal processes and estimate various probe bin yields. However, multi-dimensional information has not yet been fully utilized from both PCM data and probe bins. In this paper, we proposed a canonical correlation analysis in order to investigate the relationship between multiple PCM variables and various probe bin variables. Polynomial regression was also employed as a methodology for maximizing the performance yield based on the results of the canonical correlation analysis. Two conclusions were drawn from the results of this research. First, the PCM variables that affected the probe bins were contact resistance, sheet resistance, and Isat_P4H as well as threshold voltage (Vt) during the process tuning step. Second, the typical values of Vtl_P4H and Isat_P4H should be changed in order to maximize the performance yield. The proposed method can be used for yield improvement and as a problem-solving approach for optimizing the IC process.