Ferroelectrics have been demonstrated as excellent building blocks for highperformance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In 2 Se 3 can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistanceswitching ratio of heterosynaptic memristors above 10 3 , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In 2 Se 3 channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In 2 Se 3 memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In 2 Se 3 neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.

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Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science—committee machines—in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.

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Selector device is critical in high-density cross-point resistive switching memory arrays for suppressing the sneak leakage current path. GexSe1-x based ovonic threshold switch (OTS) selectors have recently demonstrated strong performance with high on-state current, nonlinearity and endurance. Detailed study of its reliability is still lacking and the understanding on the responsible mechanisms is limited. In this work, for the first time, the endurance degradation mechanism of Ge-rich GexSe1-x OTS is identified. Accumulation of slow defects that remain delocalized at off-state and GeSe segregation/crystallization during cycling lead to the recoverable and non-recoverable leakage current, respectively. Most importantly, a refreshing program scheme is developed to recover and prevent the OTS degradation and the endurance can be therefore improved by more than five orders without adding additional material elements or process steps.