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

计算范式向神经启发计算的根本转变对于执行数据密集型应用（如图像/语音识别）具有吸引力。神经启发的结构利用了神经元节点的分布式计算和突触元素的局部存储。今天的神经元节点通常由数十个硅晶体管实现。与交叉排列的突触元件相比，硅神经元耗电和面积效率低，从而降低了计算系统的并行性。在这种情况下，如何设计一个能够有效地模拟神经元行为的单一设备（例如，集成和发射）是神经形态硬件设计的关键。本项目旨在利用强相关氧化物中的金属-绝缘体-过渡现象作为一种紧凑的神经元节点，可以自振荡，即氧化物神经元，以克服硅神经元的上述局限性。这项研究将对拥抱人工智能的社会产生深远的影响。例如，神经形态硬件的紧凑设计可以实现在节能移动平台上的智能信息处理，例如自动驾驶汽车、个性化医疗保健、可穿戴设备和智能传感器。研究与教育整合的目标是培养具有跨学科技能的本科生/研究生和下一代劳动力。这个项目的跨层性质涵盖了材料工程、半导体器件、电路器件交互和人工神经网络，为这一教育目标提供了理想的平台。该项目还计划让少数族裔和不具代表性的学生参与研究。技术转让将通过录象或现场研讨会以及学生与工业合作者的实习来进行。本研究的目标是利用强相关氧化物的挥发性和阈值开关行为来推进人工神经元器件的设计，目的是显著减少神经元节点的面积和能量，并使其与电阻性突触元件的交叉棒阵列集成兼容。该项目的范围是探索各种强相关氧化物的材料系统，特别是NbO2和SmNiO3，以展示人工神经元节点的自振荡行为。当该氧化物器件与串联的突触元件连接时，其电阻在氧化物器件的开/关动态范围内，氧化物器件与突触元件之间的节点电压开始自振荡，振荡频率表示突触电导。本项目旨在探索这种自振荡来模拟神经元的行为。为了实现上述研究目标，将进行器件制造，物理和电气表征，器件建模和电路器件协同设计，以证明该概念的可行性并进一步优化器件性能。这个项目的知识意义是双重的。从基础科学的角度研究强相关氧化物中金属-绝缘体-跃迁的物理开关机制。从应用工程的角度来看，氧化物神经元装置将与电阻交叉棒阵列集成，以演示神经网络解决实际问题，即图像模式分类。

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.