Apply Fuzzy-Logic-Based Functional-Center Hierarchies as Inference Engines for Self-Learning Manufacture Process Diagnoses

Abstract

In a production process, there are numerous systems that provide information/reports for various purposes. However, most of the knowledge for decision-making is kept in minds of experienced employees rather than exists in IT systems that can be managed systematically. Even experienced managers may make flaw/improper decisions due to the lack of must-known information, not to mention what those who are less experienced or have been urged by the pressure of time will probably do. In this paper, a fuzzy-logic-based functional center hierarchical model named Dynamic Master Logic (DML) is designed as an interview interface for representing engineers’ tacit knowledge and a self-learning model for tuning the knowledge base from historical cases. The DML representation itself can also be the inference engine in a manufacture process diagnoses expert system. A semiconductor Wafer Acceptance Test (WAT) root cause diagnostics which usually involves more than 40,000 parameters in a 500-step production process is selected to examine the DML model. In this research, it has been proven to shorten the WAT diagnostics time from 72 hours to 15 minutes with 98.5% accuracy and to save the human resource form 2 senior engineers to one junior engineer.