Prediction of plasma processes using neural network and genetic algorithm

Kim, Byungwhan; Bae, Jungki

doi:10.1016/j.sse.2005.08.003

Cited by 24 publications

(9 citation statements)

References 10 publications

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“…Apart from these two gradients, the other important training factors include the training tolerance (TT), number of hidden neurons (NHN), magnitude of initial weight distribution (MIWD). It should be noted that the effect of these training factors was once optimized for the first type of model mentioned earlier (Kim and Bae, 2005).…”

Section: Neural Networkmentioning

confidence: 99%

Prediction of plasma etch process by using actinometry-based optical emission spectroscopy data and neural network

Kim

Kwon

2009

Journal of Materials Processing Technology

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Section: Neural Networkmentioning

confidence: 99%

Prediction of plasma etch process by using actinometry-based optical emission spectroscopy data and neural network

Kim

Kwon

2009

Journal of Materials Processing Technology

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“…The p+ source and junction depth was 0 3 m. As a charge collector, an aluminum antenna with an area of 100 000 m 2 was fabricated. The experiments were conduced in a CHF 3 -CF 4 inductively coupled plasma, a widely adopted process in the etching or deposition of thin films [16]. It should be noted that in the experiments the process parameters were varied under the exposed antenna-MOSFETs for the characterization of plasma-induced charging damage.…”

Section: Experimental Datamentioning

confidence: 99%

Statistical Characterization of Process-Induced Plasma Damage

Kim

Kwon

et al. 2009

Materials and Manufacturing Processes

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During plasma processes, charging damage produces various defects in silicon oxide, thereby deteriorating device performance. Optimizing process-induced charging damage requires a computer model, as well as a quantitative analysis of process parameter effects. In this study, plasma charge damage on threshold voltage of metal-semiconductor field-effect transistors is statistically investigated. This includes the analysis of main and interaction effects of process parameters, as well as the construction of response surface models. Charging damage is characterized by means of a statistical experiment. Four types of statistical regression models are constructed. A model with the largest R-Square (R 2 ) fit of 90.6 is chosen for the response surface analysis. Analysis of the main effects revealed that radio frequency power and gas ratio are the most significant and least significant factors, respectively. Among various interaction terms, only the interaction between radio frequency power and bias is found to be influential. Meanwhile, several conflicting effects are noted as the bias power or gas ratio are varied. An optimized regression model is used to understand parameter effects on plasma charging damage.

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“…Therefore, many researchers have attempted to use GA to improve BP neural network in order to achieve the complementary advantages (Sexton, 1998;Gupta & Sexton, 1999). Some successful examples of the improved BP neural network which were optimized by GA had been reported to optimize successfully the flow stress of 304 stainless steel under cold and warm compression (Anijdan et al, 2007) or the surface roughness in end milling Inconel 718 (Ozcelik et al, 2005) or the plasma processes (Kim & Bae, 2005), etc. In literature (Zemin et al, 2010), BP neural network was used to predict punch radius based on the results of air-bending experiments of sheet metal.…”

Section: Introductionmentioning

confidence: 99%