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.

通过高级包装过程将硅技术与新兴设备相结合的三维异质整合方法可以利用高级电子/光电来利用独特的半导体组合。特别是，基于SI的人工神经元和人工突触的整合将通过最大程度地减少传感器，计算和驱动单元之间的数据传输来实现节能近传感器计算。我们的神经形态阵列将允许各种传感器获取的数据的原位处理，并将为驱动提供必要的控制信号，该信号可普遍地用于读取和处理外部刺激并相应地响应，例如原位视觉处理和机械响应。具体而言，3D集成的神经形态单元将实现高频和高功率操作，实现用于机器人，自动驾驶汽车和医疗设备的简化传感系统。因此，我们提出的异质集成系统为紧凑的神经形态边缘计算系统提供了创新的范式，该系统从中央处理单元（CPU）和图形处理单元（GPU）分散。为了实现上述目标，该提案旨在设计和展示一个芯片人造体感系统，该系统可以通过3D整合互补的金属氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 基于尖峰神经元和基于gan gan Ferroelectric高电子移动型（Fehemememts）基于人造的人造属性来模仿生物体感增强系统。设计的神经形态芯片将能够使用一维时间序列向量调节小型感觉信号。可以使用基于CMOS的尖峰神经网络（SNN）进行有效处理的原始时间序列感觉信号，以进行节能和时空编码，以克服von Neumann瓶颈。设计的神经形态芯片提供了单发计算，类似于中枢神经系统（CNS）中的生物计算。此外，Cu-Cu互连将使基于宽带的半导体基于CMOS的SNN与铁电晶体管的高密度3D整合，以实现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基于前端CMOS的神经形态芯片进行编码。编码的输出信号将基于基于FEHEMT的基于FEHEMT横杆的突触阵列直接传输到后端神经形态芯片，以编程其重量值。通过ALBN/GAN HEMT横杆阵列的解码输出电流可以超过安培的运行订单，从而使其能够驱动系统宏观运动的机械驱动，例如机械对象跟踪。我们认为，提议的混合信号神经形态阵列将允许对时间序列的感觉数据进行原气处理，从而实现了超低功率的人造体感系统的实现使用基金会的智力优点和更广泛的影响评估标准进行评估。