Existing methods of predicting mask-limited device yield based on average defect density do not account for defect size or superposition of defective dice, since to do so would require a degree of computation which is not feasible with purely manual means. This paper proposes a new algorithm for computing predicted yield, and describes a system which automates the computation of this algorithm, in addition to permitting on-line collection and storage of inspection data from an automatic photomask inspection system. The use of yield predictions obtained thereby for optimizing device yield, or for apportioning the cause of actual degraded yield between the process and the masks, is also discussed.

Date Published: 5 September 1980
PDF: 10 pages
Proc. SPIE 0221, Developments in Semiconductor Microlithography V, (5 September 1980); doi: 10.1117/12.958634

Published in SPIE Proceedings Vol. 0221:
Developments in Semiconductor Microlithography V
James W. Dey, Editor(s)