近日，美国宾夕法尼亚大学Feng, Liang课题组研究了现场可编程光子非线性。这一研究成果于2025年4月15日发表在《自然—光子学》杂志上。

在人工智能的推动下，对现场可编程设备的需求在过去十年中迅速增长。在各种最先进的平台中，可编程集成光子学是一个有前景的候选者，它提供了一种新的策略，可以大大提高数据密集型任务的计算能力。然而，介电材料的固有弱非线性响应将传统的光子可编程性限制在线性域，从而忽略了人工智能中使用的最常见和最复杂的激活函数。

研究组通过对有源半导体内的分布式载流子激发及其动力学进行细致的空间控制，将光子场可编程性的能力推向了非线性领域。利用多项式构建块的光子非线性计算架构，该现场可编程光子非线性微处理器演示了具有动态重新配置非线性连接的光子多项式网络的原位训练。该研究结果提供了一种新的范式，彻底改变了光子可重构计算，使人们能够使用多项式网络以无与伦比的简单性和效率处理复杂的任务。

附：英文原文

Title: Field-programmable photonic nonlinearity

Author: Wu, Tianwei, Li, Yankun, Ge, Li, Feng, Liang

Issue&Volume: 2025-04-15

Abstract: Propelled by advancements in artificial intelligence, the demand for field-programmable devices has grown rapidly in the last decade. Among various state-of-the-art platforms, programmable integrated photonics emerges as a promising candidate, offering a new strategy to drastically enhance computational power for data-intensive tasks. However, intrinsic weak nonlinear responses of dielectric materials have limited traditional photonic programmability to the linear domain, leaving out the most common and complex activation functions used in artificial intelligence. Here we push the capabilities of photonic field-programmability into the nonlinear realm by meticulous spatial control of distributed carrier excitations and their dynamics within an active semiconductor. Leveraging the architecture of photonic nonlinear computing through polynomial building blocks, our field-programmable photonic nonlinear microprocessor demonstrates in situ training of photonic polynomial networks with dynamically reconfigured nonlinear connections. Our results offer a new paradigm to revolutionize photonic reconfigurable computing, enabling the handling of intricate tasks using a polynomial network with unparalleled simplicity and efficiency.

DOI: 10.1038/s41566-025-01660-x

Source: https://www.nature.com/articles/s41566-025-01660-x