Collaborative Research: Large-Scale Wireless RF Networks of Microchip Sensors

合作研究：微芯片传感器的大规模无线射频网络

• 批准号: 2322600
• 负责人: Arto Nurmikko
• 金额: $ 38.36万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322600&HistoricalAwards=false
• 关键词: Collaborative; Research; Large; Scale; Wireless

The world around us is increasingly surrounded by electronic sensors. For applications such as wearable and implantable biomedical sensors there is a particular need and opportunity for unobtrusive microdevices which operate autonomously as large ensembles to map physiological activity across a body area of interest. A challenge is how to construct a wireless network whereby data from a microsensor population is transmitted, received, and decoded to unravel data, say from 1000 individual sensors. A rough analogy is that of a population of common radio-frequency tags which must be read at once by a single transceiver - with the twist that signals at each sensor location will now vary both in time and in magnitude. A brain-computer interface suggests a paradigm in this context: how to capture neuronal signals at high resolution by a population of autonomous brain implanted microsensors. Ongoing research for development of brain-machine interfaces in laboratories worldwide is focused on a number of schemes where access to thousands of points in the cortex is sought to translate brain computations to useful electronic commands e.g. for intended speech. The neurotechnology problem is three-fold: to record electrical signals from the brain unobtrusively, to transmit the data wirelessly to a body external receiver, and to decipher the multitude of signals in real time. Many cases of distributed sensing of a dynamical environment are characterized by sparsity of events whether in nature or man-made systems, neurons in the brain being an example. The proposed event-driven communication strategy enables the efficient transmission, accurate retrieval, and interpretation of sparse events across a network of thousands of wireless microsensors – using the brain as an inspiration. The proposed work is focused on an all-in-one approach to build a large scale wireless microsensor radio-frequency network. An external transceiver collects data while supplying wireless power to the sensors. Each sensor is a sub-millimeter size silicon system-on-microchip with custom circuitry designed for ‘event detection’ where time-varying sensor inputs are encoded as a series of short ‘spikes’. The brain-inspired method of encoding data from sparse events has emerged recently in so- called dynamic vision cameras. Spike train data are converted into digital form on chip and transmitted to one common receiver. Since only the event-driven spikes are transmitted through the network, the bandwidth of the communication system can be utilized very efficiently enabling a large population of sensors to be incorporated into the network.The team proposes to build a microsensor system and demonstrate low-error rate and efficient asynchronous, encoded wireless transmission in the laboratory using fabricated microchips, and to show extended applicability to thousands of nodes though simulations.Importantly, the event-sensing detection and wireless communication approach is quite suited for a neuromorphic computational approach for analyzing multisensory data; the third key element in the project. The team will show how to decode actual brain data (synthesized elsewhere from actual primate brain recordings) from a hypothetical implant composed of up to 8000 microsensors. Neuromorphic computing appears particularly suited decoding event-based data in terms of efficiency and short latency. Using available data from the primate motor cortex the team plans to show how to decode wireless signals from thousands of neurons for the prediction of planned arm and hand movement.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.

我们周围的世界越来越多地被电子传感器包围，对于可穿戴和植入式生物医学传感器等应用来说，对不显眼的微型设备有特殊的需求和机会，这些设备可以作为大型整体自主运行，以绘制感兴趣的身体区域的生理活动。面临的挑战是如何构建一个无线网络，通过该网络传输、接收和解码来自微传感器群体的数据，以解析来自 1000 个单独传感器的数据，一个粗略的类比是一群常见的射频标签。必须由单个收发器立即读取 - 每个传感器位置的信号现在都会在时间和幅度上发生变化，脑机接口提出了这种情况下的范例：如何以高分辨率捕获神经信号。世界各地实验室正在进行的脑机接口开发研究主要集中在一些方案上，这些方案旨在访问皮层中的数千个点，将大脑计算转化为有用的电子命令，例如：对于预期的语音，神经技术问题有三个方面：不引人注目地记录来自大脑的电信号，将数据无线传输到身体外部接收器，以及实时破译大量信号。动态环境的特点是事件的稀疏性，无论是自然系统还是人造系统，以大脑中的神经元为例，所提出的事件驱动通信策略能够在数千个网络中有效传输、准确检索和解释稀疏事件。无线的微传感器——以大脑为灵感。拟议的工作重点是构建大规模无线微传感器射频网络的一体化方法，外部收发器在向传感器提供无线电源的同时。亚毫米尺寸的硅微芯片系统，具有专为“事件检测”而设计的定制电路，其中随时间变化的传感器输入被编码为一系列短“尖峰”。受大脑启发的数据编码方法。稀疏事件最近出现在所谓的动态视觉相机中，在芯片上将数据转换为数字形式并传输到一个通用接收器，因为只有事件驱动的尖峰通过网络传输，因此通信系统的带宽可以提高。能够非常有效地利用，使大量传感器能够纳入网络。该团队建议构建一个微传感器系统，并使用制造的微芯片在实验室中展示低错误率和高效的异步编码无线传输，并展示扩展的适用性给成千上万的重要的是，事件传感检测和无线通信方法非常适合用于分析多感官数据的神经形态计算方法；该团队将展示如何解码实际的大脑数据（从其他地方合成）。就效率和使用可用数据的短延迟而言，神经形态计算似乎特别适合解码基于事件的数据。该团队计划展示如何从灵长类动物运动皮层解码来自数千个神经元的无线信号，以预测计划的手臂和手部运动。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和技术进行评估，被认为值得支持。更广泛的影响审查标准。