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Invention principles and contradiction matrix for semiconductor manufacturing industry: chemical mechanical polishing.

Authors :
Sheu, D.
Chen, Chia-Hung
Yu, Pang-Yen
Source :
Journal of Intelligent Manufacturing; Oct2012, Vol. 23 Issue 5, p1637-1648, 12p
Publication Year :
2012

Abstract

The classical contradiction matrix (CM) and inventive principles (IPs) developed by Altshuller were based on patents from traditional industries in the 1950s. Evidences showed that the classical contradiction matrix and inventive principles are not quite suitable for newer hightechnology industries such as semiconductor industry due to the fact that the fundamental physics of operating principles are different. To date, no research has developed any CM and IP specifically suitable for the semiconductor industry. This research, as the first step of efforts develop to suitable CM and IP for semiconductor industry using patents from Chemical Mechanical Processing (CMP) equipment and processes in the semiconductor industry. By focusing on a particular industry, we can develop a more suitable CM and IP for that particular industry and with less number of patents needed to review. The results show that a newly established preliminary CM based on merely 120 patents from 1999 to 2008 can interpret 80% of the inventive principles in a set of new patents. This is a significant improvement over the original Altshuller's original CM which can only interpret 40% with 40,000 patents studied. In addition, during this study, two existing principles were revised to reflect a broader application and three new inventive principles were identified. The contributions of this research include: 1) Revising the traditional engineering parameters to be consistent with semiconductor industry including the addition of 7 new parameters; 2) Identifying 3 new IPs and modifying 2 IPs for CMP processes; 3) Establishing a triplet representation to model any patent to facilitate future analytical studies of the contradiction matrix and IPs. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09565515
Volume :
23
Issue :
5
Database :
Complementary Index
Journal :
Journal of Intelligent Manufacturing
Publication Type :
Academic Journal
Accession number :
79959682
Full Text :
https://doi.org/10.1007/s10845-010-0466-4