利用机器学习设计光子晶体全光逻辑门  

Design Photonic Crystal All-optical Logic Gates Using Machine Learning

作　　者：陈建伟 郝然 占春连 金尚忠 张鹏举 庄新港 费丰[2] CHEN Jianwei;HAO Ran;ZHAN Chunlian;JIN Shangzhong;ZHANG Pengju;ZHUANG Xingang;FEI Feng(College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310018,China;The 41st Research Institute of China Electronics Technology Group Corporation,Qingdao 266555,China)

机构地区：[1]中国计量大学光学与电子科技学院,杭州310018 [2]中国电子科技集团公司第四十一研究所,青岛266555

出　　处：《光子学报》2023年第9期66-74,共9页Acta Photonica Sinica

基　　金：国家自然科学基金(Nos.61975182,61575174);国防技术基础项目(No.JSJL2020210A001);浙江省自然科学基金重点项目(No.LZ23F050001)。

摘　　要：为了加速光子晶体性能分析和全光逻辑门的设计,提出了利用神经网络设计基于带隙传输的光子晶体全光逻辑门。使用逆向神经网络,根据需要的群折射率、光子带隙和工作频率等光学性质,成功逆向预测光子晶体逻辑门的结构参数。仿真结果表明:该逻辑门能在时域实现AND和NOT运算;对比输入和运算输出的脉冲宽度,AND运算脉宽仅变化3.6%,实现稳定的包络和精确的“数字”逻辑运算。The all-optical logic gate is the core component of the photonic computer,optical signal processing,and all-optical network.Based on the photonic crystal,the all-optical logic gate has attracted much attention due to its simple structure,low loss,fast operation speed,and small volume.Photonic crystal waveguides can manipulate light for logical operations,which may open up new prospects for photonic computing and optical interconnection networks.However,the design of photonic crystal logic gates is still an iterative process,and the reverse acquisition of geometric structures according to requirements is the key to solving practical engineering problems.To accelerate the performance analysis of photonic crystals and the design of all-optical logic gates,a neural network design of bandgap transmission photonic crystal all-optical logic gates was proposed.In this study,TensorFlow was used as the development framework of the neural network,and a forward performance characterization and inverse structure prediction model of the photonic crystal waveguide was constructed:the forward performance characterization model had 13 fully connected layers,and the total number of parameters trained by the neural network was 197612,which can realize the timely prediction of the structure of the photonic crystal waveguide to the optical performance;the inverse structure prediction model had 26 fully connected layers,and the total number of parameters trained by the neural network was 155704,which could reversely design the structure parameters of the photonic crystal waveguide according to the required optical performance,which is helpful to solve practical engineering problems.The Intel Core i9-10940X processor and RTX 3080 Ti graphics card are used for the forward performance characterization and reverse structure prediction network,with training times of 0.2 and 0.36 hours,respectively.The coefficient of determination between the predicted and actual values of the computational neural network was 0.997 for the forward neural ne

关 键 词：光子晶体 光子计算机 逻辑门 神经网络 非线性光学 

分 类 号：O436[机械工程—光学工程]