SHF: Small: Fusion of Quantum Dot/Nanowire Based Sensors and Processors in Ultra-low-energy, Distributed-Intelligence Sensing Network

SHF：小型：超低能耗分布式智能传感网络中基于量子点/纳米线的传感器和处理器的融合

• 批准号: 1017143
• 负责人: Pinaki Mazumder
• 金额: $ 40.03万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017143&HistoricalAwards=false
• 关键词: SHF; Small; Fusion; Quantum; Dot

The purpose of the award is to develop a new nano-circuit architecture, novel circuit realization using quantum-electronic devices, and a comprehensive, vertically integrated design methodology for real-time electronic vision applications. The architecture integrates nano-optical sensors and active quantum dot processing elements into hybrid, ultra efficient, distributed intelligence systems with meta-level decision making agents. This is in contrast with conventional electronic vision systems where all decision related processing is done by an algorithmic agent only after digitization of the input from nanowire sensors. The approach employs a smooth analog-to-digital processing transition to allow en-route, hierarchical transformation of the input into simpler, digital representations of multidimensional, abstract input characteristics while the signal is on its way to the central decision making agent. A more rapid decision-making can be achieved because the agent can now directly correlate just the key features without first identifying and extracting these features or filtering out extraneous details. Such close-to-source processing also includes all other desirable benefits like high energy-efficiency, low signal degradation and small area requirement for chip implementation, in addition to high speed. Specifically, fanning-out the input to a cellular-neural-network-like architecture of active quantum-electronic analog functional units will extract and encode the key features that can then be fanned in to one or more decision agents. These units include spatio-temporal filters, velocity estimators, and image processing elements. The intellectual merits of this research include the construction of nanoscale quantum dots based cellular logic arrays capable of performing neuromorphic computation like spatiotemporal signal processing, video and image processing; and the design of a new CAD tool for optimizing the 3D nanostructures of quantum tunneling devices while performing the system-level optimization in an augmented circuit simulator developed by the principal investigator's research group. The broader impacts include development of pedagogical interdisciplinary training to the next generation of circuit engineers and supplementary didactic material---two new, definitive textbooks on nanoelectronics.

该奖项的目的是开发一种新的纳米电路架构，使用量子电子设备实现新颖的电路实现，以及用于实时电子视觉应用的全面，垂直集成的设计方法。 该体系结构将纳米光传感器和主动量子点处理元素集成到具有元级决策代理的混合，超高效，分布式智能系统中。 这与常规的电子视觉系统相反，在传统的电子视觉系统中，所有与决策相关的处理仅在数字化纳米线传感器的输入后才完成。 该方法采用平滑的模数处理过渡，允许将输入的层次转换为多维，抽象输入特征的更简单，数字表示，而信号正向中心决策代理。 可以实现更快速的决策，因为代理现在可以直接将关键功能直接关联，而无需先识别和提取这些功能或过滤无关的细节。 此类接近源的处理还包括所有其他理想的好处，例如高能效力，低信号降解和芯片实施的小面积需求，此外还包括高速。具体而言，将活性量子电子模拟功能单元的类似细胞神经网络的输入驱动到类似细胞的神经网络架构中，将提取并编码可以将其散发到一个或多个决策剂中的关键特征。这些单元包括时空过滤器，速度估计器和图像处理元素。这项研究的智力优点包括构建基于纳米量子点的蜂窝逻辑阵列，能够执行神经形态计算，例如时空信号处理，视频和图像处理；以及一种新的CAD工具的设计，用于优化量子隧道设备的3D纳米结构，同时在首席研究员研究人员开发的增强电路模拟器中执行系统级优化。 更广泛的影响包括向下一代电路工程师和补充教学材料开发教学跨学科培训 - 两本关于纳米电子学的新的，确定的教科书。