NDLI: Defect prediction for reactive ion etching using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Stokes, D. May, G. Chen, V. Lin, Y.T.
Copyright Year	1997
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Stokes, D.)
Abstract	As geometries in integrated circuits continue to decrease, the elimination of submicron defects caused by particles generated within semiconductor processes becomes more and more critical. These particles can cause surface defects which lead to reduced yield. While 100% inspection of processed wafers during fabrication provides the most accurate means for detecting these anomalies, it is also very time-consuming and costly. This cost can be mitigated through the use of automated in-situ particle monitoring systems (ISPMs) which provided real-time estimates of particle counts in process chambers for different categories of particle sizes. However, the challenge is to correlate ISPM measurements with actual surface defects. In this paper, neural network models are used to estimate the number of particles that are deposited on a semiconductor wafer based on ISPM data collected during processing in a reactive ion etching (RIE) chamber. This particle prediction methodology can lead to reduced resting costs and more accurate defect detection.
Starting Page	169
Ending Page	173
File Size	421739
Page Count	5
File Format	PDF
ISBN	0780341228
DOI	10.1109/ICNN.1997.611658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Etching Neural networks Costs Geometry Integrated circuit yield Inspection Fabrication Computerized monitoring Real time systems Semiconductor device modeling
Content Type	Text
Resource Type	Article

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