Optimization of via formation in photosensitive dielectric layers using neural networks and genetic algorithms

Kim, Tae Seon; May, G.S.

doi:10.1109/6104.778172

Cited by 17 publications

(1 citation statement)

References 22 publications

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“…Optimum solutions for plasma processing have been found using the genetic algorithm (GA) approach, in conjunction with neural network-based models of plasma behaviour [9]. We have previously demonstrated the advantages of artificial intelligence (AI) for controlling plasma processes, including the use of fuzzy logic for controlling DC bias, but process optimization was not attempted at that stage [10].…”

Section: Introduction and Strategymentioning

confidence: 99%

Optimization of plasma etch processes using evolutionary search methods within situdiagnostics

Al-Kuzee

Mizutani

Goodyear

et al. 2004

Plasma Sources Sci. Technol.

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This paper presents several approaches that have been used to control, optimize and characterize a low pressure (10-300 mTorr) plasma processing system. Methods such as contour following and differential evolution have been used to find contours of DC bias, total ion flux, ion energy flux, quadrupole mass spectrum (QMS) intensity ratios and line intensity ratios of the optical emission spectrum (OES) in argon and nitrogen plasmas. A mapping for a 4 × 4 multi-dimensional parameter space is also presented, in which the relationship between four control parameters (power, pressure, mass flow rates of two supplied gases) and four measurement outputs (DC bias, ion flux, QMS ratios and OES line intensity ratios) is determined in a plasma etching process. The use of these methods significantly reduces the time needed to re-configure the processing system and will benefit transfer of processes between different systems. A similar approach has also been used to find quickly an optimum condition for directional etching of a silicon wafer.

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Section: Introduction and Strategymentioning

confidence: 99%

Optimization of plasma etch processes using evolutionary search methods within situdiagnostics

Al-Kuzee

Mizutani

Goodyear

et al. 2004

Plasma Sources Sci. Technol.

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Artificial Intelligence in Semiconductor Manufacturing

May

Kim

Triplett

et al. 2007

Wiley Encyclopedia of Electrical and Electronics Engineering

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The sections in this article are Introduction Artificial Intelligence Tools Process Modeling Optimization Process Monitoring and Control Process Diagnosis Yield Modeling Conclusion

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Introduction to Fusion of Soft Computing and Hard Computing

Ovaska¹

2004

Computationally Intelligent Hybrid Systems

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Optimization of via formation in photosensitive dielectric layers using neural networks and genetic algorithms

Cited by 17 publications

References 22 publications

Optimization of plasma etch processes using evolutionary search methods within situdiagnostics

Optimization of plasma etch processes using evolutionary search methods within situdiagnostics

Artificial Intelligence in Semiconductor Manufacturing

Introduction to Fusion of Soft Computing and Hard Computing

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