PIC: Hybrid Photonic-Electronic Reprogrammable Reservoir Computing with Polarization Modes-enhanced Dimensionality

PIC：具有偏振模式增强维数的混合光子-电子可重编程储层计算

• 批准号: 2217453
• 负责人: Yeshaiahu Fainman
• 金额: $ 42万
• 依托单位: University of California-San Diego
• 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: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217453&HistoricalAwards=false
• 关键词: PIC; Hybrid; Photonic; Electronic; Reprogrammable

The recent success of Machine Learning methods based on brain-inspired Neuromorphic Computing (NC) to perform complex information processing tasks spiked significant research in new unconventional computational schemes such as Recurrent Neural Networks (RNNs) and RNN-based Reservoir Computing (RC) which are capable to implement parallel data processing to overcome limitations of conventional sequential computing. Particularly, decomposing the reservoir into an inner network with static weights and an output neurons layer with adaptive and trainable weights allows realization of physical RC where optical-based RC platforms are attractive due to the “speed-of-light” propagation, inherent parallelism, relatively low operation power, and the possibility to harness additional degrees of freedom such as polarization and wavelength. Furthermore, on-chip Photonic Integrated Circuit (PIC) offer enhanced light-matter interaction and modes polarization for enlarged reservoir, and interconnection with CMOS compatible electronics for power efficient electrical reprogrammable feedback. The proposed physical RC PICs are expected to impact mobile applications such as unmanned autonomous vehicles (UAV) and robotic platforms by reducing the need for communication with remote computers, thus avoiding latency and prolonging battery life.(technical description) To realize the reservoir computing (RC) processor utilizing the polarization degrees of freedom on a photonic integrated chip (PIC), we propose the following objectives: (1) numerical and theoretical study aiming to explore the effect of introducing polarization as a new degree of freedom on RC efficiency depending on the underlying architecture of PIC with the electronic feedback elements providing dynamics control; (2) design, fabricate and characterize silicon PIC interconnected with external electronic feedback, admitting the designed architectures; (3) experimentally test the PIC system with external electronic feedback to realize reprogrammable RC tasks, validate the theoretical study and evaluate its performance for relevant applications providing higher accuracy and lower energy consumption compared to state-of-the-art. Rapid prototyping and testing will be performed at UCSD with full scale runs performed at the AIM Photonics foundry. The proposed research is transformative in nature as it will: (i) greatly expand the limits of applicability of RC in CMOS compatible PIC platforms, (ii) develop a fundamental understanding on the effect of reprogrammability on the induced reservoir dynamics and the corresponding performance error, (iii) expand the current notions of both RC and the optical degrees of freedom employed for RC (e.g., polarization). The transformative broader impact of the project arises from the creation of a new much faster and more efficient RC PIC accelerator that will impact mobile applications such as UAV and robotic platforms. The project will provide scientific training for students at graduate and undergraduate levels as well as serve as a basis for outreach, education and collaborative efforts with middle and high schools. Engagement of students of diverse ethnicity, gender and economic backgrounds in Science, Technology, Engineering and Mathematics (STEM) will be continued via the ongoing RET, REU, and COSMOS activities. The program will continue developing a plug & play Integrated Photonics Education Kit (IPEK) and disseminate it to other institutions to implement hands-on classes for a large number of students with diverse origins and gender, and workforce population in the U.S.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.

近年来，基于脑启发神经形态计算(NC)的机器学习方法在执行复杂的信息处理任务方面取得了成功，这推动了对新的非传统计算方案的研究，如递归神经网络(RNNS)和基于RNN的油藏计算(RC)，这些方案能够实现并行数据处理，以克服传统顺序计算的局限性。具体地说，将储存库分解为具有静态权重的内部网络和具有自适应和可训练权重的输出神经元层允许实现物理RC，其中基于光学的RC平台由于“光速”传播、固有的并行性、相对较低的操作功率以及利用诸如偏振和波长的附加自由度的可能性而具有吸引力。此外，片上光子集成电路(PIC)提供了增强的光-物质相互作用和模式偏振，用于扩大蓄水池，并与CMOS兼容电子设备互连，以实现高能效的电气可编程反馈。(技术描述)为了在光子集成芯片(PIC)上实现利用偏振自由度的水库计算(RC)处理器，我们提出了以下目标：(1)数值和理论研究，旨在探索引入偏振作为一种新的自由度对RC效率的影响，依赖于PIC的底层体系结构，其中电子反馈元件提供动态控制；(2)设计、制作和表征与外部电子反馈互连的硅PIC，验证了所设计的结构；(3)对外部电子反馈PIC系统进行了实验测试，实现了可编程RC任务，验证了理论研究，并评估了其在相关应用中的性能，提供了比现有技术更高的精度和更低的能耗。将在加州大学圣迭戈分校进行快速原型制作和测试，并在AIM Photonics铸造厂进行全尺寸运行。所提出的研究在本质上是变革性的，因为它将：(I)极大地扩展RC在CMOS型PIC平台中的适用范围，(Ii)对可重编程性对诱导储存池动力学的影响和相应的性能误差有一个基本的理解，(Iii)扩展RC和RC所采用的光学自由度(例如，偏振)的现有概念。该项目的变革性更广泛的影响来自于创建一种新的更快、更高效的RC PIC加速器，它将影响无人机和机器人平台等移动应用程序。该项目将为研究生和本科生提供科学培训，并作为推广、教育和与初中和高中合作的基础。通过正在进行的RET、REU和COSMOS活动，将继续让不同种族、性别和经济背景的学生参与科学、技术、工程和数学(STEM)。该计划将继续开发即插即用集成光子学教育套件(IPEK)，并将其传播给其他机构，为美国不同出身、性别和劳动力人口的大量学生实施实践课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Vertical-cavity surface-emitting phase shifter

垂直腔面发射移相器

• DOI: 10.1364/oe.497606
• 发表时间: 2023
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Almutairi, Dhaifallah;Johnson, Karl;Smolyaninov, Alexei;Grieco, Andrew;Fainman, Yeshaiahu
• 通讯作者: Fainman, Yeshaiahu

Compensation of Kerr-induced impairments in silicon nitride third-harmonic generators

氮化硅三次谐波发生器中克尔引起的损伤的补偿

• DOI: 10.1364/oe.479059
• 发表时间: 2023
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Chen, Zijun;Fainman, Yeshaiahu
• 通讯作者: Fainman, Yeshaiahu

Multirate Spectral Domain Optical Coherence Tomography

• DOI: 10.1109/jphot.2023.3313521
• 发表时间: 2023-10-01
• 期刊: IEEE PHOTONICS JOURNAL
• 影响因子: 2.4
• 作者: Gaur，Prabhav;Grieco，Andrew;Fainman，Yeshaiahu
• 通讯作者: Fainman，Yeshaiahu