(Invited) Design and Modeling of Resistive Switching Devices for Neuromorphic Applications

Resistive switching devices are a critical component for the realization of computational systems that mimic biological neural systems. In biological systems, connection weights between the neurons are dependent on the time lapse between the neurons’ action potentials. Resistive switching devices (memristors) with multiple conductance states can mimic this biological spike-time-dependent plasticity (STDP) behaviour, with multiple conductance states being analogous to different synaptic weights. Additionally, memristors are one of the contenders as storage elements in non-volatile memories. The change in conductance in memristors can be broadly attributed to vacancy migration inside the switching layer (valence change memory) or the formation of metallic filaments inside the switching layer (electrochemical metallization). Electrochemical metallization-based memristors with vertical transport and small inter-electrode distances have been reported recently. Their device characteristics exhibit multiple conductance states with relatively low switching voltages, which makes them well-suited for low-power neuromorphic applications. Our work models the transport in these memristors, focusing on explaining and capturing their current-voltage characteristics. Our model also captures the switching dynamics (with an emphasis on estimating switching energies and delays) and explains the experimentally observed STDP behaviour in these devices. We have proposed models for filament growth and dissolution along one dimension (axial) and two dimensions (axial and radial). The simulation results obtained using our model (and implemented in Verilog-A) have been validated with experimental data from multiple sources. Our work demonstrates the flexibility of including different transport mechanisms in a unified framework. We have characterized valence change memory (VCM) memristors with HfO2 and HfZrO2 as switching layers. They exhibit bipolar switching, negative differential resistance, multiple conductance states, and STDP behaviour, which have been analyzed. We have also modeled the transport in these VCM-based memristors, and simulated the I-V and dynamic switching characteristics, and STDP behaviour in these devices. We have simulated arrays of, both, ECM and VCM devices using the proposed models, and tried to demonstrate a typical pattern classification problem using in-memory computation. We believe that our models provide useful insight into the design of memristors for neuromorphic and memory applications.