Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.

Motion detection is a primary visual function, crucial for the survival of animals in nature. Direction‐selective (DS) neurons can be found in multiple locations in the visual neural system, both in the retina and in the visual cortex. For instance, the DS ganglion cell in the retina provides a real‐time response to moving objects, which is much faster than the image recognition executed in the visual cortex. Such in‐retina biological signal processing capability is enabled by the spatiotemporal correlation within different receptive fields of the DS ganglion cells. Taking inspiration from the biological DS ganglion cells in the retina, the motion detection is demonstrated in an artificial neural network made of volatile resistive switching devices with short‐term memory effects. The motion detection arises from the spatiotemporal correlation between the adjacent excitatory and inhibitory receptive fields with short‐term memory synapses, closely resembling the physiological response of DS ganglion cells in the retina. The work supports real‐time neuromorphic processing of sensor data by exploiting the unique physics of innovative memory devices.

Understanding the switching mechanism of the volatile resistive switching random access memory (RRAM) device is important to harness its characteristics and further enhance its performance. Accurate modeling of its dynamic behavior is also of deep value for its applications both as selector and as short-term memory synapse for future neuromorphic applications operating in temporal domain. In this work, we investigate the switching and retention (relaxation) processes of the Ag-based metallic filamentary volatile resistive switching devices. We find that the switching process can be modeled by the ionic drift under electric field, while the retention process can be modeled by the ionic diffusion along the filament surface driven by the gradient of surface atomic concentration. Through further theoretical analysis, we also find that the ionic drift and ionic diffusion can be unified within the general Einstein relation. To confirm this relation, we collect ionic mobility and diffusivity data from the literature using the switching and retention model. Finally, we show that the read voltage dependent retention time can be explained by the competition between the ionic drift and diffusion flux.

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing–dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

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