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| Content Provider | IEEE Xplore Digital Library | 
|---|---|
| Author | Stokes, D. May, G.S. | 
| Copyright Year | 1999 | 
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Stokes, D.) | 
| Abstract | Recent work has shown that neural networks offer great promise in modeling complex fabrication processes such as reactive ion etching (RIE). This paper explores the use of neural networks for real-time, model-based feedback control of RIE. This objective is accomplished in part by constructing a predictive model for the system, which can be inverted (or approximately inverted) to achieve the desired control. The efficacy of this approach is demonstrated using experimental data from a SiO/sub 2/ etch process to simulate real-time control of etch rate, uniformity, selectivity, and anisotropy. In addition, using a residual gas analysis system as a sensor, the approach is further demonstrated using actual experimental data acquired during the etch of a GaAs/AlGaAs metal-semiconductor-metal structure. In the latter case, real-time control of the etch rate of the constituent materials is investigated. | 
| Starting Page | 779 | 
| Ending Page | 783 | 
| File Size | 482426 | 
| Page Count | 5 | 
| File Format | |
| ISBN | 078035446X | 
| DOI | 10.1109/CCA.1999.807760 | 
| Language | English | 
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Publisher Date | 1999-08-22 | 
| Access Restriction | Subscribed | 
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Subject Keyword | Plasma applications Etching Neural networks Fabrication Feedback control Predictive models Anisotropic magnetoresistance Gas detectors Sensor systems Gallium arsenide | 
| Content Type | Text | 
| Resource Type | Article | 
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