A general equipment diagnostic system and its application on photolithographic sequences

Abstract

This paper presents a general diagnostic system that can be applied to semiconductor equipment to assist the operator in finding the causes of decreased machine performance. Based on conventional probability theory, the diagnostic system incorporates both shallow and deep level information. From the observed evidence, and from the conditional probabilities of faults initially supplied by machine experts (and subsequently updated by the system), the unconditional fault probabilities and their bounds are calculated. We have implemented a software version of the diagnostic system, and tested it on real photolithography equipment malfunctions and performance drifts. Initial experimental results are encouraging.

Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

“…Bayesian networks have been used in numerous applications over the past several years. Some of these applications include traffic scene analysis (Huang, 1994), general equipment diagnosis for photolithographic sequences (Leang, 1997), manufacturing and process diagnosis (Agogino, 1986), and tracking and avoidance of objects for automated vehicles (Alag, 1995). Bayesian networks have also been applied recently at Hewlett Packard for integrated circuit tester diagnosis (Mittelstadt, 1995).…”

Manufacturing Process Planning to Evaluation on Failure Causes for Lithography Machine: Analytic Hierarchy Process

“…Based on the traditional probability theory, Leang and Spanos (1997) conducted the diagnostic analysis of the machine by gathering actual operation information in lithography processing, in order to find out the probability of failure cause combination, and specific failure in some failure causes. Through the calculation of conditional probability, the probability of possible failure cause was obtained when some failures occurred.…”