Reconfigurable Diffractive Optical Neural Networks with Phase Change Material based Photonic Device

具有基于相变材料的光子器件的可重构衍射光学神经网络

• 批准号: 2316627
• 负责人: Weilu Gao
• 金额: $ 37.48万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316627&HistoricalAwards=false
• 关键词: Reconfigurable; Diffractive; Optical; Neural; Networks

Diffractive optical neural networks (DONNs) systems have gained interest as high-performance optical architectures to perform machine learning tasks. Toward the ideal DONNs systems, there is a lack of energy-efficient diffractive pixel unit and accurate software models. This project employs one type of nonvolatile material called phase change material (PCM) and address two major challenges, substantial switching energy and multilevel operations, to develop PCM-based diffractive devices. This project also develops an accurate model by incorporating interlayer and intralayer effects. The research findings from this project can find broad photonic and optoelectronic applications, such as in communication, computation, and quantum technologies. This project also expands participation in science, technology, engineering, and math (STEM) through training and education activities in the laboratory, classroom, and through outreach programs. The goal of these activities is to develop a diverse future STEM workforce.DONNs systems perform machine learning tasks through spatial light modulation and optical diffraction in multiple diffractive layers. However, toward the implementation of the ultimate all-optical, fully reconfigurable, and compact diffractive layers for DONNs systems, there exist technological gaps including nonvolatile reconfigurability, and accurate and trainable software models. To fill these gaps, this project employs nonvolatile chalcogenide PCMs that feature a few desirable properties, such as in-memory computing, large optical contrast, and ultrafast reconfiguration with high cyclability, to construct a near-infrared diffractive device for DONNs systems. This project aims to address following challenges, including large reconfiguration energy consumption and multilevel operation for implementing PCM-based photonic devices, as well as the discrepancy between the standard DONNs model and the compact DONNs system with PCM-based diffractive devices. Specifically, this project creates an energy-efficient and transparent electrical heater for reconfiguring PCMs using aligned carbon nanotube films with extraordinary and separately optimizable electrical, thermal, and optical properties. This project also designs, optimizes, and fabricates a multilevel reconfigurable device by only using two reliable crystalline and amorphous states in multiple PCM films. In addition, this project implements accurate and trainable DONNs models by incorporating the effects of interlayer reflection and intralayer interpixel interaction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

衍射光学神经网络 (DONN) 系统作为执行机器学习任务的高性能光学架构而引起了人们的兴趣。对于理想的 DONNs 系统，缺乏节能的衍射像素单元和准确的软件模型。该项目采用一种称为相变材料 (PCM) 的非易失性材料，并解决两个主要挑战：大量开关能量和多级操作，以开发基于 PCM 的衍射器件。该项目还通过结合层间和层内效应开发了一个精确的模型。该项目的研究成果可以找到广泛的光子和光电应用，例如通信、计算和量子技术。该项目还通过实验室、课堂的培训和教育活动以及外展计划扩大对科学、技术、工程和数学 (STEM) 的参与。这些活动的目标是培养多样化的未来 STEM 劳动力。DONN 系统通过空间光调制和多个衍射层中的光学衍射来执行机器学习任务。然而，为了实现 DONN 系统的最终全光学、完全可重构和紧凑的衍射层，还存在技术差距，包括非易失性可重构性以及准确和可训练的软件模型。为了填补这些空白，该项目采用非易失性硫族化物 PCM 来构建用于 DONN 系统的近红外衍射器件，该 PCM 具有一些理想的特性，例如内存计算、大光学对比度和具有高循环能力的超快重构。该项目旨在解决以下挑战，包括实现基于 PCM 的光子器件的大量重构能耗和多级操作，以及标准 DONNs 模型和带有基于 PCM 衍射器件的紧凑 DONNs 系统之间的差异。具体来说，该项目创建了一种节能且透明的电加热器，用于使用具有非凡且可单独优化的电、热和光学性能的对齐碳纳米管薄膜来重新配置 PCM。该项目还通过在多个 PCM 薄膜中仅使用两种可靠的晶态和非晶态来设计、优化和制造多级可重构器件。此外，该项目通过结合层间反射和层内像素间相互作用的影响，实现了准确且可训练的 DONN 模型。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。