The predictions of optoelectronic attributes of LED by neural network

Weng, Pin-Hsuan; Chen, Yu‐Ju; Wang, Shuming T.; Hwang, Rey-Chue

doi:10.1016/j.eswa.2010.02.094

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2016

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“…For instance, the genetic algorithm (GA) had been used for searching the optimal parameters of physical vapor deposition manufacturing control process [4]. Hsieh, Tong, Cui and Hwang et al proposed the film's control and estimation by using NN techniques [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Estimation of Thin Film Properties by Neural Network

Shen

Chen

Wang

et al. 2016

ACIS

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This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.

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