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.

人工神经网络是一种模仿人脑操作的机器学习技术，这些网络通常使用电子设备来实现，并且它们促成了许多使用光的神经网络（光学神经网络）的最新技术进步。计算速度甚至更快，可能达到光速。然而，与电子设备竞争的全尺寸光学网络尚未得到证明，因为它们缺乏允许人工神经网络做出决策的关键元素。在该计划中，将出现新的光学设备。开发填补了这个缺失的拼图。通过在彼此上生长不同材料的原子薄层，将创建第一个子带间神经元，这些设备将能够根据照射到它们的光量做出决策，并且最终将实现超快光学神经元。这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，并且可以实现完全不使用电子设备的信息处理。对社区产生更广泛的影响。南本德一所中学的光学推广计划，向学生介绍重要概念，并使他们有机会看到一个实际的研究实验室，它还将为来自弱势群体的本科生以及弱势群体制定一个暑期研究计划。关于非线性光学的新研究生课程。技术描述：该计划的主要目标是开发用于信息处理的新型子带间光子器件，最终形成第一个能够高速运行的基于神经网络的深度学习光学神经网络。彻底改变了通过级联线性矩阵乘法和非线性激活函数，深度神经网络可以学习许多任务，原则上，光学神经网络可以以光速执行计算，但不幸的是，光速是电子网络的数千倍。在计算网络的线性部分时，它不能如此轻松地计算非线性部分，光学非线性速度很快，但众所周知，在该程序中，将设计一种将光学元件与非线性电子元件混合在一起的纳米结构。子带间跃迁的物理原理使子带间神经元、非线性器件有望以比现有器件快得多的速度运行并且具有更低的光功率，已经开发出几种新颖的设计策略，可以实现低阈值、低功耗、高速人工神经元。，并且在该计划中，还将使用新兴材料系统在较短波长下开发神经元，以提高该概念的可扩展性和长期可行性。它将为一种全新的光学神经网络方法奠定了基础，该方法无缝地融合了电子学的最佳特性和光子学的最佳特性，尽管子带间物理学以前已被用来制造光源、探测器和传感器，但尚未实现。对计算产生影响——该项目将在光学、电气工程和计算机科学的交叉领域做出重要贡献。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进行评估，被认为值得支持。和更广泛的影响审查标准。