Browse wiki

Abstract	Most of the research performed over the pa … Most of the research performed over the past twenty years in semiconductor process modeling has relied on fairly conventional methods of scientific programming. While these are adequate for many simulation and analysis applications, the more complex problems of synthesis and diagnosis suggest the need for alternative approaches to modeling. This paper examines the breadth of potential applications of artificial intelligence (AI) to synthesis and diagnosis. Successful application of AI to this field is sparce, but enough specific examples exist to suggest high potential for future use of the approaches that are described. The paper identifies tasks in semiconductor modeling which are candidates for application of AI methodology, catalogs appropriate methodology and surveys applications at the current state-of-the-art. lications at the current state-of-the-art.
Author	John L. Mohammed +, Paul Losleben +
Bibtype	techreport +
Institution	Knowledge Systems, AI Laboratory +
Key	KSL-93-63 +
Modification dateThis property is a special property in this wiki.	1 May 2009 13:39:28 +
Month	October +
Note	Submitted to IEEE Trans. Semicond. Manuf. Stanford Integrated Circuits Lab., Manuf. Sci Tech. Note Series No. 5. +
Number	KSL-93-63 +
Tag	Computer science +
Title	Applications of Artificial Intelligence to Semiconductor Modeling +
Tr id	KSL-93-63 +
Year	1993 +
Categories	Technical Report, Publication, KSL Technical Report

	No properties link to this page.