and high density. [4][5][6][7] Moreover, memristors with analog switching behaviors can faithfully resemble biological computational elements in both structure and switching dynamics. With the intrinsic biomimetic features, memristors could act as the basic computational element in artificial neural networks and have been demonstrated with the capability of solving cognitive computing tasks with spatiotemporal complexity without complex peripheral circuits. [7] Among various material systems, 2D materials recently demonstrated memristive switching behaviors that possess biologically comparable energy consumption compared with the traditional memristors based on oxide materials. [8][9][10][11] Thanks to their atomically thin layers and planar configurations, 2D material based memristors have provided an intriguing window into the motions of ions and opportunities to achieve outstanding electrical performances. [12][13][14] It has been reported that vertical synapses built in 2D MoS 2 push the switching threshold voltages to an extremely low value of 0.1 V. [14] More recently, multiterminal memtransistor consisting of hybrid memristor and transistor were fabricated using 2D materials to realize gate-tunable heterosynaptic functionality, which could not be achieved with transitional materials. [4,15,16] In addition, the rapid development of chemical vapor deposition (CVD) technology enables wafer scale production of 2D material, paving the way for large scale integration of 2D devices. Therefore, dimensionality reduction from 3D to 2D provides an innovative way for further advancing memristor devices in both scalability and electrical performance.Despite enormous efforts have been devoted in investigating 2D material based memristors, progresses are only made on emulating various synaptic functions. Neuromorphic networks comprise layers of artificial neurons that receive, process and transmit signals, and synapses that connect the neurons and evolve to alter the connection patterns during learning. [17,18] Although artificial neurons based on traditional oxide and phase change materials have been implemented, 2D materials have their distinct advantages. [19,20] For instance, the physical properties of 2D materials can easily be modulated by multi factors, such as doping and interface engineering, 2D material based memristors have exhibited superior performance as artificial synapses for neuromorphic computing. However, 2D artificial neurons as have note been exploited as an indispensable computational element owing to the rich dynamics of neurons, which impede the construction of a 2D neuromorphic network. A methodology is developed by introducing ionic migration dynamics and electrochemical reaction into monolayer MoS 2 single crystal and a 2D artificial neuron is realized. The sophisticated electrophysiology process of leaky integrate-and-fire (LIF) is emulated by the injection and extraction of Ag + ions under an e-field in a monolayer MoS 2 device with fine-tuned channel length. Moreover, the fire frequency and ...

Memristive devices based on two-dimensional (2D) semiconducting materials have emerged as highly promising neuromorphic devices due to their intrinsic atomic body and unique properties. However, the migration and redistribution of anions induces built-in electric field at 2D materials/electrode interface. It inevitably leads to nonlinearity and saturation of conductance change, which are the key challenges of 2D materials based synaptic devices to achieve high accuracy neuromorphic applications. In this work, we report a vertical heterostructure formed by monolayer CVD-grown MoS 2 and WO 3 films, in which the WO 3 films serve as anions reservoir to steadily absorb and release sulfur anions, thus successfully overcoming the hurdles of nonlinearity and limited conductance states. We experimentally demonstrate a nearly linear change in conductance (∼1.1) and as high as 130 (∼2 7 ) weight states, which is a record among 2D materials-based synapses. Simulations prove that artificial neural network with MoS 2 /WO 3 heterostructure synapses achieves a significantly improved learning accuracy of 93.2% in MNIST handwritten digits, demonstrating the dual benefits of linearity and multilevels caused by the anion reservoir. In addition, the essential synaptic behaviors, such as potentiation/depression, paired pulse facilitation, spike-rate-dependent plasticity as well as transformation from short-term plasticity to long-term plasticity are implemented in the heterostructure device. The introduction of anion reservoir opens an effective approach to overcome the limitations of 2D materials and enhance the performance of neuromorphic devices for high-precision neuromorphic computing.

The transformation from silent to functional synapses is accompanied by the evolutionary process of human brain development and is essential to hardware implementation of the evolutionary artificial neural network but remains a challenge for mimicking silent to functional synapse activation. Here, we developed a simple approach to successfully realize activation of silent to functional synapses by controlled sulfurization of chemical vapor deposition-grown indium selenide crystals. The underlying mechanism is attributed to the migration of sulfur anions introduced by sulfurization. One of our most important findings is that the functional synaptic behaviors can be modulated by the degree of sulfurization and temperature. In addition, the essential synaptic behaviors including potentiation/depression, paired-pulse facilitation, and spikerate-dependent plasticity are successfully implemented in the partially sulfurized functional synaptic device. The developed simple approach of introducing sulfur anions in layered selenide opens an effective new avenue to realize activation of silent synapses for application in evolutionary artificial neural networks.

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This work mathematically described the growth/shrinkage dynamics of nanoscale metallic filaments in gap type atomic switch, providing a direction for studying the switching behaviors in atomic switches from a quantitative view.