Design and Parameter Optimization of Zero Position Code Considering Diffraction Based on Deep Learning Generative Adversarial Networks  

作　　者：Shengtong Wang Linbin Luo Xinghui Li 

机构地区：[1]Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China [2]Tsinghua-Berkeley Shenzhen Institute,Tsinghua University,Shenzhen 518055,China

出　　处：《Nanomanufacturing and Metrology》2024年第1期15-27,共13页纳米制造与计量（英文）

基　　金：supported by the National Natural Science Foundation of China with No.62275142;the Basic and Applied Basic Research Foundation of Guangdong Province with No.2021B1515120007.

摘　　要：Absolute measurement has consistently been the primary focus in the development of precision linear and angular displace-ment measurements.The scheme design of binary zero position codes is an important factor for absolute measurement.Designing and optimizing high-bit zero position codes with over 100 bits face considerable challenges.Simultaneously,the working parameters of zero position codes[unit code width(b),distance(d),and yaw angle(α)]remarkably affect their post-installation performance,particularly in absolute positioning and limit code application in multi-degree-of-freedom measurement schemes.This study addresses these challenges by proposing a design method for zero position codes that considers diffraction based on generative adversarial networks and aims to explore a design with increased efficiency and accuracy as well as optimization for high-bit zero position codes.Additionally,the tolerance range of zero positioning per-formance for each working parameter is examined.By leveraging the adversarial network structure,this study generates the optimization of a 150-bit code and processes the tests of the zero position code by using simulation results.The following working parameter ranges for code design are recommended on the basis of theoretical and experimental results:b greater than 10μm,d andαwithin 1000μm and 3490μrad,and avoidance of intervals with sharp changes in the full width at half maximum.The proposed code design and parameter optimization lay a solid foundation for research and engineering appli-cations in absolute measurement field and have considerable potential for generalization and wide applicability.

关 键 词：Absolute measurement Zero position code Deep learning Generative adversarial networks Tolerance range Parameter optimization 

分 类 号：TN91[电子电信—通信与信息系统]