NSF Award
2324781
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). <br/><br/>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.<br/><br/>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.
