Exploration of Neuromorphic Memristive Devices

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Recent years have seen great interest in emerging memory technologies, with resistive random access memory (RRAM) being one of the most promising candidates, with low power consumption, high-speed switching, and high-density storage in 3D arrays demonstrated. Two-terminal resistance switching elements can change resistance under appropriate voltage bias. The ability to retain the last resistance state even without a voltage bias suggests a range of potential applications including non-volatile memories. Furthermore, remarkably, these devices resemble different neuronal functions - most importantly synaptic-like plasticity by gradually changing their resistance ("synaptic weights"). By utilising the "computing-in-memory", it is possible to solve the long-lasting problem of the "von Neumann bottleneck": the need to continually shuffle data between processing cores and memory. Such systems have massive potential in delivering solutions that are far superior to existing CMOS hardware in the implementation of machine learning (ML) and machine intelligence. One of the main benefits is a significant reduction in circuit complexity and vast improvements in power efficiency. In this project, students will extensively explore the synaptic modulation and neuronal activation (two fundamental functions in artificial NNs) by construct neuromorphic functional units using and optimising RRAM devices. Synaptic functionality might include potentiation, depression, and learning such as spiking-time dependent-plasticity (STDP) and "neuronal-like" functionality will include integration and spiking. Students will have an opportunity to characterise oxide- based RRAM devices experimentally, contribute to optimisation and fabrication of the devices, and to construct physical and circuit models that will find the optimal balance between functionality and power efficiency.