Use of Evolutionary Optimization Algorithms for the Design and Analysis of Low Bias, Low Phase Noise Photodetectors

被引：7

作者：

Anjum, Ishraq Md ^{[1
]}

Simsek, Ergun ^{[1
]}

Mahabadi, Seyed Ehsan Jamali ^{[1
]}

Carruthers, Thomas F. ^{[1
]}

Menyuk, Curtis R. ^{[1
]}

Campbell, Joe C. ^{[2
]}

Tulchinsky, David A. ^{[3
]}

Williams, Keith J. ^{[3
]}

机构：

[1] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA

[2] Univ Virginia, Dept Elect & Comp Engn, Charlottesville, VA 22904 USA

[3] Naval Res Lab, Washington, DC 20375 USA

来源：

JOURNAL OF LIGHTWAVE TECHNOLOGY | 2023年 / 41卷 / 23期

关键词：

Photodetectors; Optimization; Phase noise; Doping; Mathematical models; Semiconductor process modeling; Computational modeling; Frequency combs; optimization; photodetectors; PHOTODIODE;

D O I：

10.1109/JLT.2023.3330099

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：

With the rapid advance of machine learning techniques and the increased availability of high-speed computing resources, it has become possible to exploit machine-learning technologies to aid in the design of photonic devices. In this work we use evolutionary optimization algorithms, machine learning techniques, and the drift-diffusion equations to optimize a modified uni-traveling-carrier (MUTC) photodetector for low phase noise at a relatively low bias of 5 V. We compare the particle swarm optimization (PSO), genetic, and surrogate optimization algorithms. We find that PSO yields the solution with the lowest phase noise, with an improvement over a current design of 4.4 dBc/Hz. We then analyze the machine-optimized design to understand the physics behind the phase noise reduction and show that the optimized design removes electrical bottlenecks in the current design.

引用

页码：7285 / 7291

页数：7