Dynamic resistive switching devices for neuromorphic computing

Neuromorphic systems that can emulate the structure and the operations of biological neural circuits have long been viewed as a promising hardware solution to meet the ever-growing demands of big-data analysis and AI tasks. Recent studies on resistive switching or memristive devices have suggested such devices may form the building blocks of biorealistic neuromorphic systems. In a memristive device, the conductance is determined by a set of internal state variables, allowing the device to exhibit rich dynamics arising from the interplay between different physical processes. Not only can these devices be used for compute-in-memory architectures to tackle the von Neumann bottleneck, the switching dynamics of the devices can also be used to directly process temporal data in a biofaithful fashion. In this Review, we analyze the physical mechanisms that govern the dynamic switching behaviours and highlight how these properties can be utilized to efficiently implement synaptic and neuronal functions. Prototype systems that have been used in machine learning and brain-inspired network implementations will be covered, followed with discussions on the challenges for large scale implementations and opportunities for building bio-inspired, highly complex computing systems.

Stochastic behaviour of an interface-based memristive device

A large number of simulation models have been proposed over the years to mimic the electrical behaviour of memristive devices. The models are based either on sophisticated mathematical formulations that do not account for physical and chemical processes responsible for the actual switching dynamics or on multi-physical spatially resolved approaches that include the inherent stochastic behaviour of real-world memristive devices but are computationally very expensive. In contrast to the available models, we present a computationally inexpensive and robust spatially 1D model for simulating interface-type memristive devices. The model efficiently incorporates the stochastic behaviour observed in experiments and can be easily transferred to circuit simulation frameworks. The ion transport, responsible for the resistive switching behaviour, is modelled using the kinetic Cloud-In-a-Cell scheme. The calculated current-voltage characteristics obtained using the proposed model show excellent agreement with the experimental findings.

Positive-to-negative subthreshold swing of a MOSFET tuned by the ferroelectric switching dynamics of BiFeO3

Ferroelectricity can reduce the subthreshold swing (SS) of metal-oxide-semiconductor field-effect transistors (MOSFETs) to below the room-temperature Boltzmann limit of ~60 mV/dec and provides an important strategy to achieve a steeper SS. Surprisingly, by carefully tuning the polarization switching dynamics of BiFeO3 ferroelectric capacitors the SS of a commercial power MOSFET can even be tuned to zero or a negative value, i.e., the drain current increases with a constant or decreasing gate voltage. In particular, in addition to the positive SS of lower than 60 mV/dec, the zero and negative SS can be established with a drain current spanning for over seven orders of magnitude. These intriguing phenomena are explained by the ferroelectric polarization switching dynamics, which change the charge redistributions and accordingly affect the voltage drops across the ferroelectric capacitor and MOSFET. This study provides deep insights into understanding the steep SS in ferroelectric MOSFETs, which could be promising for designing advanced MOSFETs with an ultralow and tunable SS.