中心博士生邹秀婷的工作——Visualizing and simplifying convolutional recurrent autoencoder for mismatch compensation of channel-interleaved photonic analog-to-digital converter（可视化和简化卷积循环自动编码器，实现通道交织光子模数转换系统的失配补偿）的相关成果近期被Optics Letters期刊接收发表，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（61822508）的部分资助。通过对卷积循环自动编码器（CRAE）的可视化和模块分析，简化了CRAE网络，并利用简化后的CRAE对二通道和四通道光子模数转换（PADC）系统进行失配补偿。结果表明，简化后，CARE失配补偿效果与简化前一致；对于二通道PADC系统，CRAE网络帧率从460 frames/second 提升至975 frames/second；对于四通道PADC系统，经过CRAE补偿后，无杂散动态范围从5.2 dBc 提升至24.6 dBc.
摘要: Deep learning (DL) has been used to successfully solve numerous problems and challenges in a wide range of fields. The architecture of DL is complex and treated as a black box, making it difficult to understand the principles behind it. Here, we visualize the process of compensating for time mismatches for a two-channel photonic analog-to-digital converter (PADC) by a convolutional recurrent autoencoder (CRAE) with excellent generalizability and robustness. Besides, we explore the effects of different modules of the CRAE on the generalizability. Based on the analysis of the above two operations, we simplify the CRAE and then apply it to a four-channel PADC which is a more complex channel-interleaved system. Consequently, for both PADC systems, the performance of the simplified CRAE is as good as that of the original CRAE. Moreover, for the two-channel PADC, after simplification, the frame rate of the CRAE is increased from 460 frames/second to 975 frames/second, 20,000 points in each frame. For the four-channel PADC, the spur-free dynamic range (SFDR) is enhanced to 24.6 dBc from 5.2 dBc.