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Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO 3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications., (© 2024. The Author(s).)

In valence change memory (VCM) cells, the conductance of an insulating switching layer is reversibly modulated by creating and redistributing point defects under an external field. Accurately simulating the switching dynamics of these devices can be difficult due to their typically disordered atomic structures and inhomogeneous arrangements of defects. To address this, we introduce an atomistic framework for modeling VCM cells. It combines a stochastic kinetic Monte Carlo approach for atomic rearrangement with a quantum transport scheme, both parametrized at the ab initio level by using inputs from density functional theory. Each of these steps operates directly on the underlying atomic structure. The model thus directly relates the energy landscape and electronic structure of the device to its switching characteristics. We apply this model to simulate field-induced nonvolatile switching between high- and low-resistance states in a TiN/HfO 2 /Ti/TiN stack and analyze both the kinetics and stochasticity of the conductance transitions. We also resolve the atomic nature of current flow resulting from the valence change mechanism, finding that conductive paths are formed between the undercoordinated Hf atoms neighboring oxygen vacancies. The model developed here can be applied to different material systems to evaluate their resistive switching potential, both for use as conventional memory cells and as neuromorphic computing primitives.

2D transition-metal dichalcogenides (TMDs) have been successfully developed as novel ubiquitous optoelectronics owing to their excellent electrical and optical characteristics. However, active-matrix image sensors based on TMDs have limitations owing to the difficulty of fabricating large-area integrated circuitry and achieving high optical sensitivity. Herein, a large-area uniform, highly sensitive, and robust image sensor matrix with active pixels consisting of nanoporous molybdenum disulfide (MoS 2 ) phototransistors and indium-gallium-zinc oxide (IGZO) switching transistors is reported. Large-area uniform 4-inch wafer-scale bilayer MoS 2 films are synthesized by radio-frequency (RF) magnetron sputtering and sulfurization processes and patterned to be a nanoporous structure consisting of an array of periodic nanopores on the MoS 2 surface via block copolymer lithography. Edge exposure on the nanoporous bilayer MoS 2 induces the formation of subgap states, which promotes a photogating effect to obtain an exceptionally high photoresponsivity of 5.2 × 10 4 A W -1 . A 4-inch-wafer-scale image mapping is successively achieved using this active-matrix image sensor by controlling the device sensing and switching states. The high-performance active-matrix image sensor is state-of-the-art in 2D material-based integrated circuitry and pixel image sensor applications., (© 2023 Wiley-VCH GmbH.)