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

University Of Utah, Salt Lake City UT

Investigators

Abstract

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.