Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design and Analysis of Experiments
Design and Analysis of Experiments
Modeling of silicon oxynitride etch microtrenching using genetic algorithm and neural network
Microelectronic Engineering
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A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of processed film surfaces. In this study, a prediction model of scanning electron microscopy was constructed by using a generalized regression neural network (GRNN). The SEM components examined include condenser lens 1 and 2 and Objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM characteristic, a Box-Wilson experiment was conducted. The prediction performance of GRNN was optimized by using a genetic algorithm (GA). The prediction error of GA-GRNN model is 1.96 ×10-12 at a spread range of 0.2. From an optimized model, 3D plots were generated to interpret parameter effects on SEM resolution. For the variation in CL2 and OL-Coarse, the highest resolution (R) could be achieved in all conditions except for the two large sections, larger CL2 at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained in all conditions but those at larger CL2 and smaller CL1.