Knowledge-based process control for fault detection and classification

John Scanlan; Kevin O'Leary

doi:10.1117/12.485290

1 July 2003 Knowledge-based process control for fault detection and classification

John Scanlan, Kevin O'Leary

Proceedings Volume 5044, Advanced Process Control and Automation; (2003) https://doi.org/10.1117/12.485290
Event: Advanced Microelectronic Manufacturing, 2003, Santa Clara, CA, United States

Abstract

Knowledge-based process control integrates advanced sensors with tool and process models for enhanced fault detection and classification (FDC) performance. Rather than use a statistical or template-based control model, the knowledge-based approach is constructed around core information extracted from the process itself. The approach uses data from an advanced sensor that is known to be tool and process sensitive. In this way, the process itself does much of the data compression, rather than having to rely on statistical algorithms compiled from the tool inputs. Because it works with a knowledge of the tool itself, built through observations of the sensor data as systematic changes are made to tool and process conditions, data is used to construct a fault library upon which the FDC engine is based. A fundamental tool/process health indicator reports any excursions that match those in the library. The fault is detected and classified in real time.

Citation Download Citation

John Scanlan and Kevin O'Leary "Knowledge-based process control for fault detection and classification", Proc. SPIE 5044, Advanced Process Control and Automation, (1 July 2003); https://doi.org/10.1117/12.485290

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