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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.