Simulation-based machine learning for optoelectronic device design: perspectives, problems, and prospects

被引：9
|
作者：
Piprek, Joachim [1 ]
机构：
[1] NUSOD Inst, Newark, DE 19714 USA
来源：
OPTICAL AND QUANTUM ELECTRONICS | 2021年 / 53卷 / 04期
关键词：
Machine learning; Deep learning; Neural networks; Artificial intelligence; Numerical simulation; Design optimization; Light-emitting diode; EFFICIENCY DROOP; INVERSE DESIGN; OPTIMIZATION; MODELS; CELLS; POWER;
D O I：
10.1007/s11082-021-02837-8
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Numerical simulation and machine learning represent opposite approaches to computational analysis of the real world, deductive vs. inductive. However, both methods suffer from various uncertainties and even their combination often fails to link theory and reality. Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates examples of simulation-based machine learning from a physics point of view. Strategies are suggested for achieving more realistic predictions.
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页数：9
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