CAREER: Intersubband neurons for ultrafast optical neural networks

职业：超快光学神经网络的子带间神经元

基本信息

批准号：
2349259
负责人：
David Burghoff
金额：
$ 50万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-10-01 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349259&HistoricalAwards=false
关键词：
CAREER Intersubband neurons ultrafast optical

项目摘要

An artificial neural network is a machine learning technique that mimics the operation of a human brain. These networks are typically implemented using electronics, and they have been responsible for many recent technological advancements. Neural networks that use light—optical neural networks—could potentially perform calculations even faster, potentially at the speed of light. However, full-scale optical networks competitive with electronics have not been demonstrated, as they lack the critical element that allows artificial neural networks to make decisions. In this program, new optical devices will be developed that fill in this missing puzzle piece. By growing atomically-thin layers of different materials on one another, the first intersubband neurons will be created. These are devices that will be able to make decisions based on the amount of light that hits them, and they will eventually allow for ultrafast optical neural networks to be developed. This could directly benefit many fields, as it could provide direct speed-up of many computing tasks. In addition, it could allow for information processing that does not use electronics at all! This program integrates research and education, having broader impacts on the community. It will develop an optics outreach program for a middle school in South Bend, one that introduces students to important concepts and will allow them the opportunity to see a real research lab in action. It will also develop a summer research program for undergraduates from underrepresented groups, as well as a new graduate course on nonlinear optics.Technical description:The main goal of this program is to develop new intersubband photonic devices for information processing, ultimately culminating in the first optical neural networks capable of high-speed operation. Deep learning based on neural networks has revolutionized computation. By cascading linear matrix multiplications with nonlinear activation functions, a deep neural network can learn many tasks. In principle, optical neural networks could perform calculations at the speed of light, thousands of times faster than electronic networks. Unfortunately, while light is excellent at computing the linear part of the network, it cannot so easily compute the nonlinear part. Optical nonlinearities are fast but notoriously small. In this program, a nanostructure will instead be designed that blends an optical element with a nonlinear electronic element. This program will utilize the physics of intersubband transitions to make intersubband neurons, nonlinear devices expected to operate at speeds much faster than existing devices and with lower optical powers. Several novel design strategies have been developed that can implement low-threshold, low power consumption, high-speed artificial neurons, and in this program, they will be experimentally demonstrated and characterized. Neurons will be also be developed at shorter wavelengths using an emerging material system in order to improve the scalability and long-term viability of this concept. The intellectual merit of this program is that it will lay the groundwork for a completely new approach to optical neural networks, one that seamlessly blends the best features of electronics and the best features of photonics. Though intersubband physics have previously been exploited to make sources, detectors, and sensors, they have yet to make an impact in computation—this program will do just that. It will make important contributions at the intersection of optics, electrical engineering, and computer science.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.

人工神经元网络是一种模仿人脑操作的机器学习技术。这些网络通常是使用电子产品实施的，它们负责许多最近的技术进步。使用光的神经网络 - 光学神经元网络 - 可能会更快地执行计算，这可能会以光速。但是，尚未证明与电子产品竞争的全尺度光网络，因为它们缺乏允许人工神经元网络做出决定的关键要素。在此程序中，将开发出新的光学设备，以填补这一缺失的拼图。通过种植不同材料的原子稀薄层，将创建第一个间间神经元。这些设备将能够根据击中它们的光量做出决策，最终将允许开发超快的光学神经元网络。可以直接使许多领域受益，因为它可以直接加速许多计算任务。此外，它可以允许根本不使用电子设备的信息处理！该计划整合了研究和教育，对社区产生了更大的影响。它将为南本德一所中学开发一项光学外展计划，该计划向学生介绍重要概念，并使他们有机会看到一个真正的研究实验室。它还将为来自代表性不足的小组的本科生以及非线性光学的新研究生课程制定夏季研究计划。技术描述：该计划的主要目标是开发用于信息处理的新的Intersubband光子设备，以最终在第一个能够高速运行的光学神经元网络中加剧。基于神经网络的深度学习彻底改变了计算。通过使用非线性激活函数级联线性矩阵乘法，深度神经网络可以学习许多任务。原则上，光学神经元网络可以以光速进行计算，比电子网络快数千倍。不幸的是，虽然光在计算网络的线性部分方面非常出色，但它不能那么容易计算非线性部分。光学非线性很快，但众所周知。在此程序中，将设计纳米结构将光学元件与非线性电子元件混合在一起。该程序将利用间隔过渡的物理学来制造sublband神经元，预期以速度运行的非线性设备比现有设备快得多，并且具有较低的光学功率。已经开发了几种新型的设计策略，可以实施低阈值，低功耗，高速人工神经元，在该程序中，它们将被实验证明和表征。神经元还将在较短的波长下使用新兴材料系统开发，以提高此概念的可扩展性和长期生存能力。该程序的智力优点在于，它将为一种全新的光学中性网络奠定基础，该方法无缝地融合了电子产品的最佳功能和光子学的最佳功能。尽管以前曾探索过跨带物理，以制造来源，探测器和传感器，但它们尚未对计算产生影响 - 该程序将做到这一点。它将在光学，电气工程和计算机科学的交集中做出重要的贡献。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被视为通过评估而被视为珍贵的支持。