用于衍射光学神经网络的数字化相变材料异质叠层
Digitized Phase Change Material Heterostack for Diffractive Optical Neural Network
AI总结：
本文提出由多层不同厚度非易失相变材料构成的数字化异质叠层衍射器件架构，通过实验与热分析验证其电调谐和抗热串扰能力，并评估其在可重构DONN中的机器学习性能。
摘要：
全光学且完全可重构的衍射光学神经网络（DONN）架构有望成为高吞吐量、高能效的机器学习（ML）硬件加速器，具有广泛的应用前景。然而，目前的器件与系统实现性能有限。本工作展示了一种新型衍射器件架构，称为数字化异质叠层，由多层不同厚度的非易失性相变材料（PCM）构成。该架构既能利用PCM光学特性的优势，又能缓解在单层PCM中实现多级操作所面临的挑战。概念验证实验展示了空间光调制器件中单层PCM的电调谐，热分析指导了DONN器件与系统的设计，以避免将单个异质叠层组装成阵列时产生热串扰。此外，包含三层PCM的异质叠层被设计为具有较大的相位调制范围和均匀的覆盖范围，并评估了采用所设计异质叠层的DONN系统的ML性能。所开发的器件架构为未来实现理想的能效高、快速重构且紧凑的DONN系统提供了新的机遇。
英文摘要：
All-optical and fully reconfigurable diffractive optical neural network (DONN) architectures are promising for high-throughput and energy-efficient machine learning (ML) hardware accelerators for broad applications. However, current device and system implementations have limited performance. This work demonstrates a novel diffractive device architecture, which is named digitized heterostack and consists of multiple layers of nonvolatile phase change materials (PCMs) with different thicknesses. This architecture can both leverage the advantages of PCM optical properties and mitigate challenges associated with implementing multilevel operations in a single PCM layer. Proof-of-concept experiments demonstrate the electrical tuning of one PCM layer in a spatial light modulation device, and thermal analysis guides the design of DONN devices and systems to avoid thermal crosstalk if individual heterostacks are assembled into an array. Further, heterostacks containing three PCM layers are designed to have a large phase modulation range and uniform coverage and the ML performance of DONN systems with designed heterostacks is evaluated. The developed device architecture provides new opportunities for desirable energy-efficient, fast-reconfigured, and compact DONN systems in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
