可靠,高效和可扩展的光子反向设计,通过物理启发的深度学习来实现
Guocheng Shao1,2, Tiankuang Zhou3,4, Tao Yan2
1Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China.
Nanophotonics (Berlin, Germany)
|August 13, 2025
概括
研究人员开发了一种电磁神经网络 (EMNN),用于设计芯片上计算元系统. 这种人工智能方法显著加速了设计速度,并提高了下一代硬件的准确性.
科学领域:
- 超材料和光子学
- 计算电磁学 计算机电磁学
- 在工程领域的人工智能.
背景情况:
- 芯片上计算元系统提供高速,低功耗的处理,但面临设计挑战.
- 当前的数值和分析设计方法缺乏效率和准确性.
- 对于复杂的元系统开发,需要一种新的反向设计范式.
研究的目的:
- 提出一个以物理学为灵感的深度学习架构,电磁神经网络 (EMNN),以实现高效的反向设计.
- 实现芯片上计算元系统的快速,准确和灵活设计.
- 克服基于元材料的计算现有设计方法的局限性.
主要方法:
- 开发了EMNN,包括EMNN Netlet用于本地场域解决和Huygens-Fresnel Stitch用于预测连锁.
- 利用EMNN进行基于任意输入字段和结构的直接,快速和准确的全波场预测.
- 应用EMNN来设计能够识别手写数字和语音命令的元系统.
主要成果:
- 与分析模型相比,EMNN的设计速度提高了17000倍.
- 与数值模型相比,EMNN减少了两个数量级的建模误差.
- 在EMNN设计中展示了可解释性,概括能力和高保真性.
结论:
- EMNN为计算元系统的反向设计提供了一个高效,可靠和灵活的范式.
- 深度学习与物理原理的整合增强了设计能力.
- EMNN适用于复杂的大型设备,如芯片上的光学衍射网络,先进的计算元系统.
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