Exploiting Metal-Insulator-Transition in Strongly Correlated Oxides as Neuron Device for Neuro-Inspired Computing

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A radical shift in computing paradigm towards the neuro-inspired computing is attractive for performing data-intensive applications such as image/speech recognitions. The neuro-inspired architecture leverages the distributed computation in the neuron nodes and the localized storage in the synaptic elements. The neuron node today is generally implemented by tens of silicon transistors. Compared to the crossbar array of synaptic elements, the silicon neuron is power-hungry and area-inefficient, thereby reducing the parallelism of computing system. In such context, how to design a single device that can efficiently emulate the neuronal behavior (e.g. integrate-and-fire) is critical to the neuromorphic hardware design. This project aims to exploit the metal-insulator-transition phenomenon in strongly correlated oxides as a compact neuron node that can self-oscillate, namely oxide neuron, to overcome the aforementioned limitations of silicon neuron. The proposed research will have a profound impact on the society that is embracing the artificial intelligence. For instance, a compact design of neuromorphic hardware may enable intelligent information processing on power-efficient mobile platforms, e.g. autonomous vehicle, personalized healthcare, wearable devices, and smart sensors. The objective of the research and education integration is to train undergraduate/graduate students and next-generation workforce with interdisciplinary skills. The cross-layer nature of this project ranging from materials engineering, semiconductor device, circuit-device interaction and artificial neural network provides an ideal platform for this educational goal. The project also plans to engage minority and unrepresentative students in research. Technology transfer will be performed through video or on-site seminars and student internships with industrial collaborators.The goal of this research is to advance the artificial neuron device design by exploiting the volatile and threshold switching behavior in strongly correlated oxides, with the purpose of significantly reducing the area and energy of the neuron node, and making it compatible for the integration with crossbar array of resistive synaptic elements. The scope of the project is to explore various material systems of the strongly correlated oxides, in particular, NbO2 and SmNiO3 to demonstrate the self-oscillation behavior in the artificial neuron node. When such oxide device is connected with a series synaptic element whose resistance is within the on/off dynamic range of the oxide device, the node voltage between the oxide device and the synaptic element will start self-oscillation, and the oscillation frequency represents the synaptic conductance. This project aims to explore such self-oscillation to emulate the integrate-and-fire neuronal behavior. To achieve the aforementioned research goal, device fabrication, physical and electrical characterization, device modeling, and circuit-device co-design will be performed to demonstrate the feasibility of the concept and further optimize the device performance. The intellectual significance of this project is two folded. From the fundamental science perspective, the physical switching mechanism of metal-insulator-transition in strongly correlated oxides will be investigated. From the applied engineering perspective, the oxide neuron device will be integrated with the resistive crossbar array for demonstration of a neural network for solving a practical problem, i.e. the image pattern classification.