Atomically thin layered materials such as MoS2 have future versatile applications in low power electronics. Here, we demonstrate the growth of a salt-assisted large scale, high-quality monolayer MoS2 toward the realization of a high-performance hysteresis-free field-effect transistor (FET). Density functional theory calculations are implemented to monitor the effects of the Schottky barrier and metal-induced gap states between our metal electrodes and MoS2 for achieving high carrier transport. The role of absorbed molecules and oxide traps on the hysteresis are studied in detail. For the first time, a hysteresis-free intrinsic transistor behavior is obtained by an amplitude sweep pulse I–V measurement with varying pulse widths. Under this condition, a significant enhancement of the field-effect mobility up to 30 cm2 V−1 s−1 is achieved. Moreover, to correlate these results, a single-pulse time-domain drain current analysis is carried out to unleash the fast and slow transient charge trapping phenomena. Our findings on the hysteresis-free transfer characteristic and high intrinsic field-effect mobility in salt-assisted monolayer MoS2 FETs will be beneficial for future device applications in complex memory, logic, and sensor systems.

deep learning algorithms which requires highly parallel matrix computing with massive data storage. However, the conventional Von-Neumann computing architecture with physically separated memory and processing units impinges severe criticality (such as Von-Neumann bottleneck, memory wall) when it is considered for advanced AI algorithms. [3,4] In another paradigm, 'the human brain', capable of self-learning, self-adaptivity with super parallelism, and fault tolerance could perform complex tasks such as learning, reasoning, and memorizing with a negligible power consumption provides an opportunity to subvert the conventional Von-Neumann computing architecture. [5] For instance, neuromorphic computing based on the neural architecture of the human brain can imitate the complex learning process more efficiently with very little power capable of achieving an unprecedented breakthrough in AI-based information technology. The artificial synapses mimicking the biological synapse of the brain are the workhorse of such neuromorphic computing architecture emulating the synaptic functions such as learning/memory behavior and synaptic plasticity. In this essence, tremendous research is being concerted to construct high-performing artificial synapses with efficient, stable, reliable synaptic functionalities with various controlling parameters such as electric field, magnetic field, optical stimuli, temperature, and so on. [6][7][8][9][10] Amidst, optoelectronic artificial synapses protractedly improve the synaptic functionality, owing to the controllable synergistic modulation of device conductance by optical as well as electrical stimulus. Furthermore, the utilization of optical signals provides some extraordinary characteristics, such as higher bandwidth, ultra-fast propagation speed, and anti-crosstalk, which are beneficial to resolving the challenges associated with bulky circuitry integration and issues inherited by the conventional neuromorphic circuits. [11,12] From another perspective, recently, there has been an upsurge of demand for in-memory computing where processing can be performed within the memory itself, thereby the latency between memory and processing is completely eliminated. [3,13,14] Additionally, the in-memory logic and arithmetic operations could reduce the complexity of integrated circuits and drastically The hardware implementation of advanced artificial intelligence (AI) technology based on complex deep learning and machine learning algorithms is constricted by the limitation of conventional Von-Neuman architecture. Emerging neuromorphic computing architecture based on the human brain with in-memory computing capability could instigate unprecedented breakthroughs in AI technology. In this pursuit, 2D MoS 2 optoelectronic artificial synapse imitating complex biological neuromorphic behavior such as short/ long-term memory, paired-pulse facilitation, and long-term depression-potentiation is proposed and demonstrated. Furthermore, the broadband sensitivity of the device can be utilized to emulate Pavlov'...

Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO2/Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO2 memristor shows excellent performance of digital resistive switching with a memory window of order ∼103. Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO2-based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC.