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.

作为执行机器学习任务的高性能光学体系结构，衍射光学神经网络（DONNS）系统已引起了兴趣。对于理想的Donns系统，缺乏节能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）的非挥发性材料，并针对两个主要的挑战，实质性的转换能量和多级操作来开发基于PCM的衍射设备。该项目还通过结合层间和内层效应来开发准确的模型。该项目的研究结果可以找到广泛的光子和光电应用，例如在通信，计算和量子技术中。该项目还通过实验室，教室和外展计划中的培训和教育活动来扩大科学，技术，工程和数学（STEM）的参与。这些活动的目的是开发多样化的未来的STEM劳动力。donns系统通过空间光调制和多个衍射层的光学衍射执行机器学习任务。但是，为了实施最终的全光，完全可重新配置和紧凑的衍射层，存在技术差距，包括非挥发性可重构性以及准确且可训练的软件模型。为了填补这些空白，该项目采用非挥发性辣奶油蛋白酶PCM，具有一些理想的特性，例如内存计算，大型光学对比度和具有高环境的超快重新配置，以构建Donns系统的近红外衍射设备。该项目旨在应对以下挑战，包括用于实施基于PCM的光子设备的大型重新配置能源消耗和多级操作，以及具有基于PCM的衍射设备的标准Donns模型与紧凑型Donns系统之间的差异。具体而言，该项目创建了一种节能和透明的电加热器，用于使用带有非凡且可分别优化的电气，热和光学特性的对齐的碳纳米管膜重新配置PCM。该项目还仅在多个PCM膜中使用两个可靠的晶体和无定形状态来设计，优化和制造可重构设备。此外，该项目通过结合层间反射和类上像素的互动的影响来实现准确，可训练的donns模型。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。