CRII: FET: 3D Printed Application-Specific Neuromorphic Circuits: Design, Fabrication, and Implementation

Wayne State University, Detroit MI

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Modern silicon-based CMOS systems face limitations in terms of processing speeds and energy consumption that can no longer be addressed via Moore’s-law-driven scaling-down efforts. The main inherent challenge in conventional von Neumann computer architecture is the latency caused by the data transfer between computer processing and memory units, also known as von Neumann bottleneck. Neuromorphic computing, emulating the energy-efficient and highly-parallel operations of the human brain, stands as a promising platform to address this challenge. Neuromorphic circuitry built on memristor devices enables efficient information storage in the form of resistance states, emulation of biological synaptic functionalities, and in-memory computing (i.e. via matrix-vector multiplication). Large-scale adoption of neuromorphic computing technology is currently hindered by multifaceted challenges in the materials, device and algorithm levels that are exacerbated by long-prototyping cycles of conventional microfabrication techniques. This NSF proposal aims to develop a data-driven, high-throughput and customizable manufacturing platform for neuromorphic circuits, based on nanoscale 3D printing, that will enable design freedom in both the materials and device architecture levels. The project’s broader impacts include a new generation of application-specific computational hardware that is indispensable for fueling the future implementations of AI and critical to increase U.S. competitiveness in the IC industry. The project will also support a multitude of interdisciplinary research and teaching activities at undergraduate and K12 levels with a specific focus on the involvement of underrepresented communities. The project aims to implement a nanoscale 3D printing technique to develop application-specific neuromorphic circuits. This goal will be enabled through a state-of-the-art 3D printer that achieves sub-micron resolutions via the two-photon polymerization technique, and supports a range of functional and adjustable printing resins. A novel Bayesian optimization approach will be executed to uncover the optimum set of material and process parameters that will yield neuromorphic devices with desired characteristics. An integrated electrical characterization framework will be utilized to explore the synaptic functionalities and device performance metrics of 3D printed memristor crossbar arrays. Finalized application-specific neuromorphic circuitry will undertake the hardware-level benchmarking on image classification of standard hand-digit datasets and real-life tumor-cell image data. Use of a nanoscale 3D printing platform for complex neuromorphic circuitry fabrication will enable new opportunities for customization and scalable manufacturing of application-specific computational hardware systems. 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.