Excellent synaptic behavior of lithium-based nano-ionic transistor based on optimal WO2.7 stoichiometry with high ion diffusivity.

In this study, we introduce a lithium (Li) ion-based three-terminal (3-T) synapse device using WO x as a channel. Our study reveals a key stoichiometry of WO2.7 for excellent synaptic characteristics that is related to Li-ion diffusivity. The open-lattice structure formed by oxygen deficiency promoted Li-ion injection and diffusion. The optimized stoichiometry and improved Li-ion diffusivity were confirmed by x-ray photoelectron spectroscopy analysis and cyclic voltammetry, respectively. Furthermore, the transient conductance change that inevitably occurs in ion-based synaptic transistors was resolved by applying a two-step voltage pulse scheme. As a result, we achieved a symmetric and linear weight-update characteristic with reduced program/erase operation time.

One transistor-two resistive RAM device for realizing bidirectional and analog neuromorphic synapse devices.

In this paper, we propose a one transistor-two resistive RAM (RRAM) (1T2R) device to overcome the non-ideal switching behavior of artificial synapse devices, such as the unidirectional and abrupt change in the conductance. Our findings reveal that the 1T2R device can exhibit bidirectional conductance changes using unidirectional switching RRAMs. Thus, we introduce a unidirectional but analog switching Cu-based RRAM device (Cu/Cu2-X S/WO3-X /W) having an internal voltage suppressor (Cu2-X S) to realize a bidirectional and analog 1T2R synapse device. The synaptic behaviors of the 1T2R device are calculated using the subthreshold region of an NMOSFET. In addition, we improve the on/off conductance ratio and conductance change linearity owing to the nonlinear current transition characteristics of the subthreshold region. Finally, we demonstrate that an ideal synaptic behavior can be achieved through the 1T2R device even when non-ideal switching RRAM elements are used.

Microstructural engineering in interface-type synapse device for enhancing linear and symmetric conductance changes.

The origins of the nonlinear and asymmetric synaptic characteristics of TiO x -based synapse devices were investigated. Based on the origins, a microstructural electrode was utilized to improve the synaptic characteristics. Under an identical pulse bias, a TiO x -based synapse device exhibited saturated conductance changes, which led to nonlinear and asymmetric synaptic characteristics. The formation of an interfacial layer between the electrode and TiO x layer, which can limit consecutive oxygen migration and chemical reactions, was considered as the main origin of the conductance saturation behavior. To achieve consecutive oxygen migration and chemical reactions, structural engineering was utilized. The resultant microstructural electrode noticeably improved the synaptic characteristics, including the unsaturated, linear, and symmetric conductance changes. These synaptic characteristics resulted in the recognition accuracy significantly increasing from 38% to 90% in a neural network-based pattern recognition simulation.

Understanding of proton induced synaptic behaviors in three-terminal synapse device for neuromorphic systems.

In this study, we investigate a proton-based three-terminal (3-T) synapse device to realize linear weight-update and I-V linearity characteristics for neuromorphic systems. The conductance states of the 3-T synapse device can be controlled by modulating the proton concentration in the WOx channel. Therefore, we estimate the dynamic change of proton concentration in the channel region, which directly affects synaptic behaviors. Our findings indicate that the supply of an excess number of protons from the SiO2-H electrolyte and low proton diffusivity in the WOx channel result in asymmetric and non-linear weight-update characteristics. In addition, though the linear I-V characteristics can be obtained using non-stoichiometric WOx, we observe that significant oxygen deficiency in the channel region increases the operating current levels. Thus, based on this information, we introduce optimized conditions of each component in the 3-T synapse device and shape of the gate voltage pulses. As a result, an excellent classification accuracy is achieved using linear weight-update and I-V linearity characteristics under optimized device and pulse conditions.

Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system.

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

Controllable quantized conductance for multilevel data storage applications using conductive bridge random access memory.

In this paper, we investigate the quantized conduction behavior of conductive bridge random access memory (CBRAM) with varied materials and ramping rates. We report stable and reproducible quantized conductance states with integer multiples of fundamental conductance obtained by optimizing the voltage ramping rate and the Ti-diffusion barrier (DB) at the Cu/HfO2 interface. Owing to controlled diffusion of Cu ions by the Ti-DB and the optimized ramping rate, through which it was possible to control the time delay of Cu ion reduction, more than seven levels of discrete conductance states were clearly observed. Analytical modeling was performed to determine the rate-limiting step in filament growth based on an electrochemical redox reaction. Our understanding of the fundamental mechanisms of quantized conductance behaviors provide a promising future for the multi-bit CBRAM device.