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

• 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

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，并且它们对最近的许多技术进步负责。使用光的神经网络光学神经网络有可能以更快的速度进行计算，可能达到光速。然而，与电子产品竞争的全尺寸光网络尚未得到证明，因为它们缺乏允许人工神经网络做出决策的关键要素。在这个项目中，将开发新的光学设备来填补这一缺失的拼图。通过在彼此上生长原子级薄的不同材料层，将创建第一个子带间神经元。这些设备将能够根据击中它们的光量做出决定，它们最终将允许开发超快光学神经网络。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。此外，它还可以实现完全不使用电子设备的信息处理！该计划将研究和教育结合起来，对社区产生了更广泛的影响。它将为南本德的一所中学制定一个光学推广计划，向学生介绍重要的概念，并让他们有机会看到一个真实的研究实验室的运作。它还将开发一个夏季研究计划，为本科生从代表性不足的群体，以及一个新的研究生课程的非线性光学。技术说明：该计划的主要目标是开发新的子带间光子器件的信息处理，最终在第一个光学神经网络能够高速运行的高潮。基于神经网络的深度学习彻底改变了计算。通过级联线性矩阵乘法与非线性激活函数，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进行计算，比电子网络快数千倍。不幸的是，虽然light在计算网络的线性部分方面非常出色，但它不能很容易地计算非线性部分。光学非线性是快速的，但众所周知的小。在这个计划中，纳米结构将被设计成将光学元件与非线性电子元件混合在一起。该计划将利用子带间跃迁的物理学来制造子带间神经元，这是一种非线性器件，预计其运行速度将比现有器件快得多，并且具有较低的光功率。已经开发了几种新的设计策略，可以实现低阈值，低功耗，高速人工神经元，在这个程序中，他们将实验证明和特点。神经元也将使用新兴的材料系统在更短的波长下开发，以提高这一概念的可扩展性和长期可行性。该计划的智力价值在于，它将为光学神经网络的全新方法奠定基础，这种方法无缝融合了电子学的最佳功能和光子学的最佳功能。尽管子带间物理学以前已经被用来制造光源、探测器和传感器，但它们还没有在计算中产生影响，而这个程序将做到这一点。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Integrated nonlinear photonics in the longwave-infrared: A roadmap

长波红外中的集成非线性光子学：路线图

• DOI: 10.1557/s43579-023-00435-1
• 发表时间: 2023
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Ren, Dingding;Dong, Chao;Burghoff, David
• 通讯作者: Burghoff, David

Band-Structure-Engineered Electronic-Photonic Nonlinear Activation Functions

• DOI: 10.1103/physrevapplied.18.064038
• 发表时间: 2022-12
• 期刊: Physical Review Applied
• 影响因子: 4.6
• 作者: Zheheng Xu;D. Burghoff

Optical-Pump Terahertz-Probe Spectroscopy of the Topological Crystalline Insulator Pb 1–x Sn x Se through the Topological Phase Transition

拓扑晶体绝缘体 Pb 1–x Sn x Se 通过拓扑相变的光泵太赫兹探针光谱

• DOI: 10.1021/acsphotonics.1c01717
• 发表时间: 2022
• 期刊: ACS Photonics
• 影响因子: 7
• 作者: Xiao, Zhenyang;Wang, Jiashu;Liu, Xinyu;Assaf, Badih A.;Burghoff, David
• 通讯作者: Burghoff, David

Low-loss hybrid germanium-on-zinc selenide waveguides in the longwave infrared

长波红外低损耗混合硒化锗锌波导

• DOI: 10.1515/nanoph-2023-0698
• 发表时间: 2024
• 期刊: Nanophotonics
• 影响因子: 7.5
• 作者: Ren, Dingding;Dong, Chao;Høvik, Jens;Khan, Md Istiak;Aksnes, Astrid;Fimland, Bjørn-Ove;Burghoff, David
• 通讯作者: Burghoff, David

Frequency combs in optically injected terahertz ring quantum cascade lasers

• DOI: 10.1063/5.0173912
• 发表时间: 2023-08
• 期刊: APL Photonics
• 影响因子: 5.6
• 作者: Md Istiak Khan;Zhenyang Xiao;S. Addamane;D. Burghoff