[论文解读] Integrated lithium niobate microwave photonic processing engine

Hanke Feng, Tong Ge+10 more|arXiv (Cornell University)|Jun 26, 2023

Photonic and Optical Devices被引用 4

一句话总结

本文提出了一种用于微波光子处理的单片集成铌酸锂平台，可在高达92 Gsps的速率下实现高速、低功耗的模拟信号计算。其在时间域积分与微分运算中实现了98.1%的准确度，展示了在常微分方程求解、超宽带信号生成以及光子辅助医学图像分割方面的应用，相较于电子方案展现出数量级的速度与功耗优势。

ABSTRACT

Integrated microwave photonics is an intriguing field that leverages integrated photonic technologies for the generation, transmission, and manipulation of microwave signals in chip-scale optical systems. In particular, ultrafast processing and computation of analog electronic signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters, microwave signal processing, and image recognition. An ideal photonic platform for achieving these integrated MWP processing tasks shall simultaneously offer an efficient, linear and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power, and a low-loss functional photonic network that can be configured for a variety of signal processing tasks, as well as large-scale, low-cost manufacturability to monolithically integrate the two building blocks on the same chip. In this work, we demonstrate such an integrated MWP processing engine based on a thin-film lithium niobate platform capable of performing multi-purpose processing and computation tasks of analog signals up to 92 giga samples per second at CMOS-compatible voltages. We demonstrate high-speed analog computation, i.e., first- and second-order temporal integration and differentiation with computing accuracies up to 98.1 %, and deploy these functions to showcase three proof-of-concept applications, namely, ordinary differential equation solving, ultra-wideband signal generation and high-speed edge detection of images. We further leverage the image edge detector to enable a photonic-assisted image segmentation model that could effectively outline the boundaries of melanoma lesion in medical diagnostic images, achieving orders of magnitude faster processing speed and lower power consumption than conventional electronic processors.

研究动机与目标

• 开发一种芯片级、单片化的集成微波光子处理平台，具备高保真度与低延迟。
• 利用电光调制与低损耗光子电路，实现微波与光信号的高速模拟计算。
• 在低电压下实现与CMOS兼容的运行，同时保持高线性度与高效率。
• 展示多功能信号处理在真实应用场景中的应用，如图像边缘检测与微分方程求解。
• 实现超高速、低功耗的光子辅助图像分割，用于医学诊断。

提出的方法

• 该平台采用薄膜铌酸锂技术，在单芯片上集成电光调制器与低损耗光子波导。
• 利用电光相位调制，在与CMOS兼容的电压下实现高带宽、线性的微波到光信号转换。
• 可重构光子网络支持动态实现积分与微分等信号处理功能。
• 系统利用时间域处理计算模拟信号的一阶与二阶导数及积分。
• 通过光子时间域微分实现图像边缘检测，实现实时医学图像边界勾勒。
• 光子处理器与机器学习推理模型接口连接，取代电子计算完成图像分割。

实验结果

研究问题

• RQ1 单片铌酸锂平台是否能在92 Gsps速率下，以CMOS兼容电压实现高保真度、高速度的模拟信号处理？
• RQ2 光子时间域积分与微分在模拟信号计算中能否实现超过98%的准确度？
• RQ3 光子辅助图像分割能否在速度与能效方面超越电子处理器，用于医学影像？
• RQ4 该平台通过全光计算求解常微分方程的效率如何？
• RQ5 该系统是否可在无需硬件重新设计的情况下，重新配置以支持多种信号处理任务？

主要发现

• 该平台在模拟信号的一阶与二阶时间域积分与微分运算中实现了98.1%的准确度。
• 信号处理速率最高达92 Gsps，支持对高带宽微波与光信号的实时处理。
• 系统实现了光子辅助图像分割，能以显著更低的延迟与功耗勾勒出黑色素瘤病灶边界，优于电子方案。
• 利用光子微分器作为关键构建模块，成功实现了超宽带信号生成。
• 该平台可在单芯片上实现可重构的信号处理功能，包括常微分方程求解与边缘检测，具备低损耗与高线性度。
• 在单片薄膜铌酸锂芯片上集成电光调制器与光子电路，实现了可扩展、低成本的制造。