Two-dimensional (2D) semiconductors, especially transition metal dichalcogenides (TMDs), have been envisioned as promising candidates in extending Moore’s law. To achieve this, the controllable growth of wafer-scale TMDs single crystals or periodic single-crystal patterns are fundamental issues. Herein, we present a universal route for synthesizing arrays of unidirectionally orientated monolayer TMDs ribbons (e.g., MoS2, WS2, MoSe2, WSe2, MoSxSe2-x), by using the step edges of high-miller-index Au facets as templates. Density functional theory calculations regarding the growth kinetics of specific edges have been performed to reveal the morphological transition from triangular domains to patterned ribbons. More intriguingly, we find that, the uniformly aligned TMDs ribbons can merge into single-crystal films through a one-dimensional edge epitaxial growth mode. This work hereby puts forward an alternative pathway for the direct synthesis of inch-scale uniform monolayer TMDs single-crystals or patterned ribbons, which should promote their applications as channel materials in high-performance electronics or other fields.

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Compared to conventional artificial vision systems, a biological visual system has an exclusive highly parallel architecture [4] that can respond directly to light stimuli and integrate adaptive detection and preprocessing functions. [5,6] Therefore, researchers have made a lot of efforts to simulate the behavior of biological visual systems using advanced devices. [7][8][9][10][11][12] This requires devices capable of converting optical signals into electrical signals over a wide spectral range with specific neuromorphic functions, such as short-term plasticity (STP), [13] longterm plasticity (LTP), [14] and paired impulse facilitation (PPF). [15] Previous reports have proposed several channel materials to achieve these functions, such as perovskite, [14,16] amorphous oxide thin film, [8,9,17] organic thin-film [18] or some 2D materials. [19] Although lightstimulated synaptic behaviors have been demonstrated, most of them [8,9,[19][20][21] are limited by the complex structures, such as the application of a floating gate, the additional trapping layer, and the integrated structures. These methods inevitably increase power consumption and limit integration. To simplify the device structure, novel mechanism is urgently needed. In addition, short-wavelength infrared (SWIR) light is less affected by atmospheric scattering than visible light, so that has a unique advantage in many important fields including night vision and all-weather imaging. Unfortunately, so far, there is hardly any photonic synaptic device that can operate in SWIR range. Different from many other semiconducting materials and most 2D materials, α-In 2 Se 3 is a 2D ferroelectric semiconductor [22,23] and can respond to SWIR light (up to 1800 nm) due to the oxygen-associated defects. [24] In addition, optical control of ferroelectric domain wall movement has been demonstrated in α-In 2 Se 3 due to its semiconductor property. [25] These limited results suggest that α-In 2 Se 3 might be promising for SWIR optical neuromorphic devices.Here, we demonstrate a novel mechanism for photonic synaptic device based on WSe 2 /In 2 Se 3 heterostructure. Using photoinduced ferroelectric polarization switching in α-In 2 Se 3 nanosheets, we realize the essential light-tunable synaptic functions such as STP, LTP, and PPF, and for the first time, the response wavelength reaches SWIR range (up to 1800 nm). We observe that the synaptic behaviors in these devices are Neuromorphic visual sensory and memory systems that can sense, learn and memorize optical information have great potential in many areas such as image recognition and autonomous driving. However, most current artificial neuromorphic vision technology is suffering from large power consumptions (>100 pJ per switching), high circuitry complexity, and difficulty in miniaturization due to the physical separation of the optic sensing, processing, and memory units. Here, a photonic neuromorphic device based on WSe 2 /In 2 Se 3 van der Waals (vdW) heterostructure is developed to meet the requirements of high-perfor...

The conventional von-Neumann architecture suffers from large power consumption, high circuitry complexity, and difficulty in miniaturization due to the physical separation of the processing and memory units. To overcome this bottleneck, neuromorphic computing has been proposed, which can work with ultralow power consumption and without any need for a bus for transferring data. Synaptic transistors are a fundamental part of the neuromorphic system, which can integrate signal processing and storage. However, a relatively poor performance of the reported synaptic devices, such as the nonlinear weight update, small dynamic range, and higher energy consumption than that of biological synapses (∼10 fJ), hinders the development of energy-efficient neuromorphic systems. Here, we demonstrate the excellent performance of a top-gated synaptic transistor based on α-In2Se3 nanosheets with a thickness of less than 10 nm. Their outstanding performances include an ultralow power consumption of 3.36 fJ per spike response, a large dynamic range of 158, and near-zero nonlinearity. In addition, a simulated neural network based on our synaptic transistor shows excellent pattern recognition accuracy. After 120 online learning cycles, the pattern recognition accuracy reaches 92.1%, which is close to the ideal accuracy of 93.2%. Such a high-performance synaptic transistor implies the great potential of two-dimensional ferroelectric semiconductors in future neuromorphic computing systems.

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