Enhancement of Synaptic Characteristics Achieved by the Optimization of Proton-Electron Coupling Effect in a Solid-State Electrolyte-Gated Transistor.

Presently, the 3-terminal artificial synapse device has been in focus for neuromorphic computing systems owing to its excellent weight controllability. Here, an artificial synapse device based on the 3-terminal solid-state electrolyte-gated transistor is proposed to achieve outstanding synaptic characteristics with a human-like mechanism at low power. Novel synaptic characteristics are accomplished by precisely tuning the threshold voltage using the proton-electron coupling effect, which is caused by proton migration inside the electrolyte. However, these synaptic characteristics are degraded because traps at the interface of channel/electrolyte disturb the proton-electron coupling effect. To minimize degradation, the oxygen plasma treatment is performed to reduce interface traps. As a result, symmetric weight updates and outstanding synaptic characteristics are achieved. Furthermore, high repeatability and long-term plasticity are observed at low operating power, which is essential for artificial synapses. Therefore, this study shows the progress of artificial synapses and proposes a promising method, a low-power neuromorphic system, to achieve high accuracy.

Hysteresis Modulation on Van der Waals-Based Ferroelectric Field-Effect Transistor by Interfacial Passivation Technique and Its Application in Optic Neural Networks.

2D semiconductor-based ferroelectric field effect transistors (FeFETs) have been considered as a promising artificial synaptic device for implementation of neuromorphic computing systems. However, an inevitable problem, interface traps at the 2D semiconductor/ferroelectric oxide interface, suppresses ferroelectric characteristics, and causes a critical degradation on the performance of 2D-based FeFETs. Here, hysteresis modulation method using self-assembly monolayer (SAM) material for interface trap passivation on 2D-based FeFET is presented. Through effectively passivation of interface traps by SAM layer, the hysteresis of the proposed device changes from interface traps-dependent to polarization-dependent direction. The reduction of interface trap density is clearly confirmed through the result of calculation using the subthreshold swing of the device. Furthermore, excellent optic-neural synaptic characteristics are successfully implemeted, including linear and symmetric potentiation and depression, and multilevel conductance. This work identifies the potential of passivation effect for 2D-based FeFETs to accelerate the development of neuromorphic computing systems.

Nitrogen-Induced Enhancement of Synaptic Weight Reliability in Titanium Oxide-Based Resistive Artificial Synapse and Demonstration of the Reliability Effect on the Neuromorphic System.

With the significant technological developments in recent times, the neuromorphic system has been receiving considerable attention owing to its parallel arithmetic, low power consumption, and high scalability. However, the low reliability of artificial synapse devices disturbs calculations and causes inaccurate results in neuromorphic systems. In this paper, we propose a stable resistive artificial synapse (RAS) device with nitrogen-doped titanium oxide (TiOx:N)-based resistive switching (RS) memory. The TiOx:N-based RAS, compared to the TiOx-based RAS, demonstrates more stable RS characteristics in current-voltage (I-V) and pulse measurements. In terms of resistance variability, the TiOx:N-based RAS demonstrates five times lower resistance variability at 1.38%, compared to 6.68% with the TiOx-based RAS. In addition, we verified the relation between the neuromorphic system and the resistance reliability of the synapse device for the first time. The pattern recognition simulation is performed using an artificial neural network (ANN) consisting of artificial synapse devices using the Modified National Institute of Standards and Technology dataset. In the simulation, the ANN with the TiOx:N-based RAS exhibited significant pattern recognition accuracy of 64.41%, while the ANN with TiOx-based RAS demonstrated only low recognition accuracy of 22.07%. According to the results of subsequent simulations, the pattern recognition accuracy exponentially decreases when the resistance variability exceeds 5%. Therefore, for implementing a stable neuromorphic system, the synapse device in the neuromorphic system has to maintain low resistance variability. The proposed nitrogen-doped synapse device is suitable for neuromorphic systems because reliable resistance variability can be obtained with only simple process steps.

Ultralow Schottky Barrier Height Achieved by Using Molybdenum Disulfide/Dielectric Stack for Source/Drain Contact.

Energy barrier formed at a metal/semiconductor interface is a critical factor determining the performance of nanoelectronic devices. Although diverse methods for reducing the Schottky barrier height (SBH) via interface engineering have been developed, it is still difficult to achieve both an ultralow SBH and a low dependence on the contact metals. In this study, a novel structure, namely, a metal/transition-metal dichalcogenide (TMD) interlayer (IL)/dielectric IL/semiconductor (MTDS) structure, was developed to overcome these issues. Molybdenum disulfide (MoS2) is a promising TMD IL material owing to its interface characteristics, which yields a low SBH and reduces the reliance on contact metals. Moreover, an ultralow SBH is achieved via the insertion of an ultrathin ZnO layer between MoS2 and a semiconductor, thereby inducing an n-type doping effect on the MoS2 IL and forming an interface dipole in the favorable direction at the ZnO IL/semiconductor interfaces. Consequently, the lowest SBH (0.07 eV) and a remarkable improvement in the reverse current density (by a factor of approximately 5400) are achieved, with a wide room for contact-metal dependence. This study experimentally and theoretically validates the effect of the proposed MTDS structure, which can be a key technique for next-generation nanoelectronics.