ABSTRACT
Multi-valued logic (MVL) technology that utilizes more than two logic states has recently been reconsidered because of the demand for greater power saving in current binary logic systems. Extensive efforts have been invested in developing MVL devices with multiple threshold voltages by adopting negative differential transconductance and resistance. In this study, a reconfigurable, multiple negative-differential-resistance (m-NDR) device with an electric-field-induced tunability of multiple threshold voltages is reported, which comprises a BP/ReS2 heterojunction and a ReS2 /h-BN/metal capacitor. Tunability for the m-NDR phenomenon is achieved via the resistance modulation of the ReS2 layer by electrical pulses applied to the capacitor region. Reconfigurability is verified in terms of the function of an MVL circuit composed of a reconfigurable m-NDR device and a load transistor, wherein staggered-type and broken-type double peak-NDR device operations are adopted for ternary inverter and latch circuits, respectively.
ABSTRACT
Recently, three-terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read-out and parallel-access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two-terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e-field-dependent movement of mobile ions in the ion-gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (Gmax /Gmin ) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of Gmax /Gmin and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of ≈99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy-efficient and high-speed data processing.
ABSTRACT
We propose a flexible artificial synapse based on a silicon-indium-zinc-oxide (SIZO)/ion gel hybrid structure directly fabricated on a polyimide substrate, where the channel conductance is effectively modulated via ion movement in the ion gel. This synaptic operation is possible because of the low-temperature deposition process of the SIZO layer (<150°C) and the surface roughness improvement of the poly(4-vinylphenol) buffer layer (12.29â1.81 nm). The flexible synaptic device exhibits extremely stable synaptic performance under high mechanical (bending 1500 times with a radius of 5 mm) and electrical stress (application of voltage pulses 104 times) without any degradation. Last, a sensory-neuromorphic system for sign language translation, which consists of stretchable resistive sensors and flexible artificial synapses, is designed and successfully evaluated via training and recognition simulation using hand sign patterns obtained by stretchable sensors (maximum recognition rate, 99.4%).
ABSTRACT
Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far. Here, we investigated the effects by (i) the formation temperature of the ferroelectric material, poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) and (ii) the nature of the contact metals (Ti, Cr, Pd) of the FeFET on the operating performance of a FeFET-based artificial synapse in terms of various synaptic performance indices. Excellent ferroelectric properties were induced by maximizing the size and coverage ratio of the ß-phase domains by annealing the P(VDF-TrFE) film at 140 °C. A metal that forms a relatively high barrier improved the dynamic range and nonlinearity by suppressing the contribution of the tunneling current to the post-synaptic current. Subsequently, we studied the influence of the synaptic characteristics on the training and recognition tasks by using two MNIST datasets (fashion and handwritten digits) and the multi-layer perceptron concept of neural networks.
