Collaborative Research: FuSe: Metaoptics-Enhanced Vertical Integration for Versatile In-Sensor Machine Vision

视觉可能是人类最重要的知觉，因为大脑的大部分认知功能都致力于处理视觉信息。尽管最近取得了进展，但与人类优越的视觉感知能力相比，今天的视觉传感器仍然相当原始。此外，深度学习和人工智能(AI)的快速发展引发了新一轮机器视觉浪潮，越来越多的图像数据被生成和消费，这些数据不是由人类生成和消费的，而是由边缘设备来执行分类、识别和感知等智能任务。受生物系统的启发和机器视觉的巨大需求的激励，这个项目研究了一种集成和整体的方法来构建可以为特定领域的任务量身定做的多功能视觉系统。它的目标是为光学、图像传感器和视觉处理器的视觉传感器创建一个垂直集成的设计堆栈。该项目预计将预示人工智能驱动的视觉系统的新范式，并展示解决关键工程挑战的技术，从自动驾驶汽车的实时视觉自适应到持续环境监测的近零能效。此外，该项目的教育和劳动力发展活动培育了一个开源硬件社区，以提高可访问性并深化超越传统学科划分的合作，以及建设对国家安全和供应链安全至关重要的视觉传感器行业国内人才的能力。该项目的研究目标是为一种新型的机器视觉系统奠定科学和工程基础，该系统探索纳米光子超材料和互补金属氧化物半导体(CMOS)电路的混合集成，并协同利用嵌入图像传感器中的计算亚表面和模拟域编码器的固有计算能力。我们在系统层和物理域中抽象设计旋钮、建模交互和权衡的原则性方法将为未来的多物理半导体器件集成提供更多的信息。我们将深入探讨在处理流水线上使用互补的固有物理域操作来优化分布计算的关键概念。我们的端到端设计框架是为了跨越多个异类物理域的建模/仿真基础设施和设计工具链之间的鸿沟而特意创建的。将支持机器学习的特征选择嵌入到光电垂直集成中的核心原则可能会对传感器丰富的智能物理平台的设计产生重大影响，在这些平台中，资源限制与严格的延迟要求相吻合。该项目开发的技术将加速支持AI的硬件，以满足数据激增带来的巨大计算需求，使一系列新兴行业广泛受益，如机器视觉即服务、智能物联网基础设施、数据驱动传感和成像。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。

Vision is perhaps the most important human perception, as the majority of the brain’s cognitive function is dedicated to processing visual information. Despite recent advancements, today’s vision sensors remain quite primitive when compared to the superior ability of human visual perception. Moreover, the rapid development of deep learning and artificial intelligence (AI) has unleashed a new wave of machine vision, where increasing amounts of image data are generated and consumed, not by humans, but by edge devices to perform intelligent tasks such as classification, recognition, and perception. Inspired by the biological system and motivated by the huge demand of machine vision, this project investigates an integrated and holistic approach to building versatile vision systems that can be tailored for domain-specific tasks. It aims to create a vertically-integrated design stack for vision sensors across optics, image sensors, and vision processors. The project is expected to herald a new paradigm of AI-driven vision systems and demonstrate technology to address pivotal engineering challenges from real-time visual adaptivity in self-driving cars to near-zero energy efficiency in persistent environmental monitoring. In addition, the project’s education and workforce development activities foster an open-source hardware community to boost accessibility and deepen collaboration beyond the traditional discipline divides, as well as to build up the capacity of domestic talents in vision sensor industry, critical to national security and supply chain safety. The research objective of this project is to create the scientific and engineering foundations for a novel machine vision system that explores the hybrid integration of nanophotonic metamaterials and complementary metal-oxide semiconductor (CMOS) circuits and synergistically leverages the intrinsic computing capability of computational metasurface and analog-domain encoder embedded inside the image sensors. Our principled approach to abstracting design knobs and modeling interactions and tradeoffs across the system layers and physical domains will inform future “More than Moore” multi-physics semiconductor device integration. We will delve into the key concept of optimally distributing computation along the processing pipeline with complementary intrinsic physical-domain operations. Our end-to-end design framework is deliberately created to bridge the divide between modeling/simulation infrastructure and design toolchains across multiple heterogeneous physical domains. The core principles of embedding machine-learning-enabled feature selection with optical/electrical vertical integration could have a major impact on the design of sensor-rich intelligent physical platforms where resource constraints coincide with strict latency requirements. The technology developed in this project will turbocharge AI-enabled hardware to satisfy the tremendous computational demand imposed by data proliferation, broadly benefiting a range of burgeoning industries such as machine vision as a service, smart IoT infrastructure, data-driven sensing and imaging.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.