Collaborative Research: CMOS+X: 3D integration of CMOS spiking neurons with AlBN/GaN-based Ferroelectric HEMT towards artificial somatosensory system

合作研究：CMOS X：CMOS 尖峰神经元与 AlBN/GaN 基铁电 HEMT 的 3D 集成，用于人工体感系统

• 批准号: 2324780
• 负责人: Kyusang Lee
• 金额: $ 24万
• 依托单位: University of Virginia Main Campus
• 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324780&HistoricalAwards=false
• 关键词: Collaborative; Research; CMOS+X; 3D; integration

Three-dimensional heterogeneous integration approaches that combine silicon technology with emerging devices via advanced packaging processes can leverage unique semiconductor combinations for advanced electronics/optoelectronics. In particular, the integration of Si-based artificial neurons and artificial synapses will enable energy-efficient near-sensor computing by minimizing data transfer between sensor, computing, and actuation units. Our neuromorphic array will allow for the in-situ processing of data acquired by various sensors and will provide necessary control signals for actuation that can be universally used to read and process external stimuli and respond accordingly, such as in-situ vision processing and mechanical response. Specifically, 3D integrated neuromorphic unit will enable high-frequency and high-power operation, realizing a simplified sensing-to-action system for robots, autonomous vehicles, and medical devices. Thus, our proposed heterogeneously integrated system provides an innovative paradigm for a compact neuromorphic edge-computing system that is decentralized from central processing units (CPUs) and graphic processing units (GPUs). To achieve the above goal, the proposal aims to design and demonstrate an on-chip artificial somatosensory system that can emulate the biological somatosensory system via 3D integration of complementary metal-oxide-semiconductor (CMOS)-based spike neurons and GaN ferroelectric high electron mobility transistors (FeHEMTs) based artificial synapses. The designed neuromorphic chip will be able to modulate small sensory signals with a one-dimensional time-series vector. The raw time-series sensory signals can be efficiently processed with a CMOS-based Spiking Neural Network (SNN) for energy-efficient and spatiotemporal encoding to overcome the Von Neumann bottleneck. The designed neuromorphic chips provide one-shot computation, analogous to the biological computing in the central nervous system (CNS). Furthermore, Cu-Cu interconnection will enable the high density 3D integration of the CMOS-based SNN with ferroelectric transistors based on wide-bandgap semiconductors for in-situ processing of the input stimulus to trigger mechanical actuation. The time-series data captured by the image sensor will be encoded through the front-end CMOS-based neuromorphic chip in a spiking domain. The encoded output signals will be directly transmitted to the back-end neuromorphic chip based on the FeHEMT crossbar-based synpatic array to program its weight value. The decoded output current through the AlBN/GaN HEMT crossbar array can exceed an order of mangitude of an ampere, allowing it to drive mechanical actuation for system macro-motion, such as mechanical object tracking. We believe the proposed mixed-signal neuromorphic array will allow for the in-situ processing of time-series sensory data, leading to the realization of an ultra-low-power artificial somatosensory system that provides power-efficient and spontaneous computing from sensing and data processing to reaction for widespread applications including AIoT and robotics.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.

通过先进封装工艺将硅技术与新兴器件相结合的三维异构集成方法可以利用独特的半导体组合来实现先进电子/光电子学。特别是，基于硅的人工神经元和人工突触的集成将通过最大限度地减少传感器、计算和驱动单元之间的数据传输来实现节能的近传感器计算。我们的神经形态阵列将允许对各种传感器获取的数据进行原位处理，并为驱动提供必要的控制信号，可普遍用于读取和处理外部刺激并做出相应的响应，例如原位视觉处理和机械响应。具体来说，3D集成神经形态单元将实现高频和高功率操作，为机器人、自动驾驶车辆和医疗设备实现简化的传感到动作系统。因此，我们提出的异构集成系统为从中央处理单元（CPU）和图形处理单元（GPU）分散的紧凑型神经形态边缘计算系统提供了创新范例。为了实现上述目标，该提案旨在设计和演示一种片上人工体感系统，该系统可以通过基于互补金属氧化物半导体（CMOS）的尖峰神经元和GaN铁电高电子迁移率的3D集成来模拟生物体感系统基于晶体管（FeHEMT）的人工突触。设计的神经形态芯片将能够用一维时间序列向量调制小的感觉信号。原始时间序列传感信号可以通过基于 CMOS 的尖峰神经网络 (SNN) 进行高效处理，以实现节能和时空编码，从而克服冯·诺依曼瓶颈。设计的神经形态芯片提供一次性计算，类似于中枢神经系统（CNS）中的生物计算。此外，Cu-Cu 互连将使基于 CMOS 的 SNN 与基于宽带隙半导体的铁电晶体管实现高密度 3D 集成，用于输入刺激的原位处理以触发机械驱动。图像传感器捕获的时间序列数据将通过前端基于 CMOS 的神经形态芯片在尖峰域中进行编码。编码后的输出信号将直接传输到基于 FeHEMT crossbar 突触阵列的后端神经形态芯片，以编程其权重值。通过 AlBN/GaN HEMT 交叉阵列的解码输出电流可以超过安培数量级，使其能够驱动系统宏观运动的机械驱动，例如机械物体跟踪。我们相信，所提出的混合信号神经形态阵列将允许对时间序列传感数据进行原位处理，从而实现超低功耗人工体感系统，该系统可通过传感和数据提供节能且自发的计算该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。