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12 March 2024 Model parameters extraction for 850 nm GaAs/AlGaAs laser diodes using simple deep neural network
Jung-Tack Yang, Hyewon Han, Tae-Kyung Kim, An-Sik Choi, Woo-Young Choi
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Proceedings Volume PC12880, Physics and Simulation of Optoelectronic Devices XXXII; PC1288007 (2024) https://doi.org/10.1117/12.3000804
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Accurately determining numerical values for key model parameters for any semiconductor devices is extremely important for analyzing the device characteristics and model-based device design optimization. However, their experimental determination can be very difficult since measurement results involve interaction of many parameters and isolating the influence of a single parameter is often not possible. One of the ways to solve this issue is deep learning. We achieve accurate determination of key laser diode model parameters such as internal loss, Auger coefficient, and free-carrier absorption coefficient of a fabricated ridge-waveguide 850 nm GaAs/AlGaAs laser diode(LD) applying the trained deep neural network (DNN). We use a LD TCAD simulator, PICS3D, for producing training and testing data. The accuracy of our approach is confirmed by comparing the simulation result with the actual measurement result for the LD L-I characteristics using extracted model parameters by DNN.
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Jung-Tack Yang, Hyewon Han, Tae-Kyung Kim, An-Sik Choi, and Woo-Young Choi "Model parameters extraction for 850 nm GaAs/AlGaAs laser diodes using simple deep neural network", Proc. SPIE PC12880, Physics and Simulation of Optoelectronic Devices XXXII, PC1288007 (12 March 2024); https://doi.org/10.1117/12.3000804
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KEYWORDS
Semiconductor lasers
Chemical elements
Neural networks
Education and training
Instrument modeling
Model-based design
Light absorption
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