Collaborative Research: RUI: Natural Bio-organic Resistive Random Access Memory Based Synaptic Devices

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Two essential challenges faced globally by computing systems today are tremendous energy consumption and electronic wastes. One potential solution to simultaneously address these two issues is by “brain-like” and “green” neuromorphic computing with energy-efficient operation and biodegradable disposals. Neuromorphic computing systems require hardware components capable of mimicking human synapse - the basic building block of biological neural networks, while natural bio-organic materials derived from living or once-living organisms such as plants, animals or microbial materials are renewable, sustainable, biocompatible, biodegradable, and abundant in nature. The proposed research will advance the development of nanoscale, ultrahigh-density and wafer-level manufacturing of natural bio-organic materials based resistive random access memory through nanofabrication and machine learning, and implementation of bio-organic materials based resistive memory in neural networks with high accuracy and efficiency for “green” neuromorphic systems. This project has great impacts on US and global societies and provides many societal benefits. The neuromorphic systems using bio-organic materials based resistive memory are desirable for stretchable, flexible and wearable electronics in personal health and biomedical applications, and address the sustainable and environmental issues brought by excessive exploitation of non-renewable resources for electronics and disposal of electronic devices. The interdisciplinary nature of this research project covers the understanding and practice in nanotechnology, non-volatile memory, neuron and synapse, neuromorphic computing systems and machine learning, which provide a perfect venue for integration of research and education. Minority, female and high school students will be mentored to perform research in nanotechnology and machine learning. A virtual reality based interactive system will be developed to provide trainings of resistive memory and synaptic device fabrication in a virtual cleanroom environment. Workshops will be organized for broadening dissemination and community outreach.The research aims to address technological challenges hampering the development of bio-organic materials based resistive memory and artificial synaptic devices. These challenges include the fabrication of nanoscale, high-density and scalable bio-organic materials based resistive memory and synaptic devices and incorporation of these devices in the neural network with high accuracy and efficiency. In this project, advanced nanotechnology and nanofabrication techniques will be developed to fabricate nanometer-sized crossbar electrodes for nanoscale and high-density bio-organic materials based resistive memory. Machine learning algorithms will be employed to study the correlation of biomaterial film process and property, device switching characteristics and synaptic behaviors. Synaptic architectures based on nanoscale bio-organic materials based resistive memory will be developed to emulate synaptic plasticity and synaptic efficacy. Implementation of bio-organic materials based resistive memory and synaptic devices in neural networks and evaluation of the learning capability will be carried out by leveraging a coherent hardware and software co-design. This project is potentially transformative and will achieve a breakthrough in the realization of nanoscale, ultrahigh-density and wafer-level manufacturing of resistive switching memory and artificial synaptic devices based on natural bio-organic materials. The research outcomes will expedite device development by accurate process optimization and establish a fundamental understanding of natural bio-organic materials based resistive switching memory and synaptic devices when used in the neural networks for “green” neuromorphic computing 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.