Electronic Synaptic Devices Research Articles

Highly reliable multilevel resistive switching in a nanoparticulated In2O3 thin-film memristive device

The present report deals with the development of a cost-effective, solution-processable and nanoparticulated In2O3 thin-film memristive device for application in multilevel resistive random access memory (RRAM). The structural, morphological, elemental and electrical characterizations are carried out with the help of x-ray diffractometry, scanning electron microscopy, photoluminescence spectroscopy, Fourier transform infrared spectroscopy, x-ray photoelectron spectroscopy and a memristor characterization system. The electrical characterizations reveal that the nanoparticulated In2O3 thin-film memristive device is free from the electroforming process and shows self-rectifying and self-compliance memory effects. Furthermore, we have demonstrated multilevel resistive switching with excellent memory properties of endurance (10 000 switching cycles) and retention (2 ks). Interestingly, the device shows a good memory window (10 for all cases), and multilevel resistance states are not degraded during endurance and retention memory tests. The uniformity and stability in multilevel resistive switching are further confirmed by statistical calculations. Furthermore, the nanoparticulated In2O3 thin-film memristive device mimics the property of paired-pulse facilitation of a biological synapse. A detailed analysis of electrical results suggests that the Schottky effect is responsible for the device conduction. In a nutshell, the nanoparticulated In2O3 memristive device is suitable for future multilevel RRAM application and is a promising candidate for development of an electronic synaptic device for application in neuromorphic computing.

Resistive switching and synaptic properties modifications in gallium-doped zinc oxide memristive devices

The massively parallel computing capabilities of the human brain can be mimicked with the help of neuromorphic computing approach and this can be achieved by developing the electronic synaptic device. In the present work, we have synthesized gallium-doped ZnO thin films using a cost-effective hydrothermal method and characterized the thin films using field-emission scanning electron microscopy and energy-dispersive X-ray spectroscopy. Furthermore, gallium-doped ZnO memristive devices were developed using standard procedure and electrically characterized for the neuromorphic application. In particular, resistive switching and synaptic properties of gallium-doped ZnO thin films were investigated. The bipolar resistive switching with an analog memory like behavior was observed in the developed memristive devices. In the present case, good synaptic properties, endurance, and retention characteristics were observed for 0.5% Ga doped memristive device. Our results suggested that the synaptic weight, potentiation-depression, and symmetric Hebbian learning can be tuned with properly engineering the ZnO memristive device with appropriate gallium doping. The detailed analysis of I-V results suggested that resistive switching is occurred due to Ohmic and Schottky conduction mechanisms.

Fabrication of synaptic memristor based on two-dimensional material MXene and realization of both long-term and short-term plasticity

Compared with conventional computation relying on the von Neumann architecture, brain-inspired computing has shown superior strength in various cognitive tasks. It has been generally accepted that information in the brain is represented and formed by vastly interconnected synapses. So the physical implementation of electronic synaptic devices is crucial to the development of brain-based computing systems. Among a large number of electronic synaptic devices, the memristors have attracted significant attention due to its simple structure and similarities to biological synapses. In this work, we first use two-dimensional material MXene as a resistive material and fabricate an electronic synapse based on a Cu/MXene/SiO<sub>2</sub>/W memristor. By using the unique properties of MXene, the conductance of the memristor can be modulated by the accumulation or reflux of Cu<sup>2+</sup> at the physical switching layer, which can vividly simulate the mechanism of bio-synapses. Experimental results show that the Cu/MXene/SiO<sub>2</sub>/W memristor not only achieves stable bipolar analog resistance switching but also shows excellent long-term and short-term synaptic behaviors, including paired-pulse facilitation (PPF) and long-term potential/depression. By adjusting the pulse interval, the PPF index will change accordingly. In a biological system, the short-term plasticity is considered to be the key point for performing computational functions while the long-term plasticity is believed to underpin learning and memory functions. This work indicates that Cu/MXene/SiO<sub>2</sub>/W memristor with both long-term and short-term plasticity will have great application prospects for brain-inspired intelligence in the future.

Development of self-rectifying ZnO thin film resistive switching memory device using successive ionic layer adsorption and reaction method

In the present report, a simple and cost-effective successive ionic layer adsorption and reaction method is employed to develop self-rectifying ZnO thin film memory device. The nature of pinched hysteresis loop and frequency dependent I–V characteristics depicts that the developed device behaves like a memristive device. Moreover, significant pinched hysteresis loop at 1 MHz was observed which could be further exploited for the development of new class of high-frequency circuits by using ZnO memristive device. The observed analog memory with scan rate dependent synaptic weights behavior suggests that the ZnO memristive device is a potential candidate for the development of electronic synaptic devices for neuromorphic computing application. Furthermore, multilevel resistive switching with good memory window was obtained at 0.2 V read voltage. The developed device switched successfully in consecutive 10 k resistive switching cycles and can retain multilevel resistance states over 1000 s without any observable degradation in the resistance states. The insights drawn from electrical characterization indicates that the device charge and charge–magnetic flux relations depend upon the frequency of the applied signal. Furthermore, we have presented the criteria for differentiating the experimental device as a memristor or memristive device based on the nature of time domain charge and double valued charge–magnetic flux relation. The resistive switching effect of the present device is manifested due to the unified effect of the Ohmic and Schottky conduction mechanisms.

An electronic synaptic device based on HfO2TiOx bilayer structure memristor with self-compliance and deep-RESET characteristics

We reported on a Ti/HfO2/TiOx/Pt memristor with self-compliance, deep-RESET characteristics and excellent switching performance, including ultrafast program/erase speed (10 ns), a large memory window (103) and good pulse endurance (107 cycles). The self-compliance and deep-RESET characteristics are beneficial for protecting the device from permanent breakdown in both SET and RESET processes especially under the pulse operation mode. In addition to bistable state switching, we also achieved multiple or even continuous conductance state switching under a DC sweep and a pulse-train operation mode in the Ti/HfO2/TiOx/Pt memristor, which can be seen as a substitution of a biological synapse. The capability of continuous modulation conductance (synaptic weight) in the Ti/HfO2/TiOx/Pt memristor was investigated and the potentiation and depression characteristics of the synaptic weight could be precisely tuned by the number or amplitude of the input pulse-train. Moreover, clear experimental evidence of short-term plasticity (STP) and long-term plasticity (LTP) in a single memristor was also demonstrated. Increasing the pulse amplitude or width, or decreasing the interval of two adjacent pulses of the input pulse-train resulted in the memristor behavior transitioning from STP to LTP. The realization of those important synaptic functions in the Ti/HfO2/TiOx/Pt memristor may be suitable for applications in artificial neural systems.

Engineering synaptic characteristics of TaOx/HfO2 bi-layered resistive switching device

We performed various pulse measurements on an atomic layer deposited (ALD) HfO2-based resistive switching random access memory (RRAM) device and investigated its electronic synaptic characteristics. Unlike requirements for RRAM device application, to achieve the multi-state conductance changes required for the synaptic device, we employed additional sputtered TaOx thin film formation on the ALD HfO2 switching medium, which leads to engineering the concentration of oxygen vacancies and modulating the conductive filaments. With this TaOx/HfO2 bi-layered device, we attained gradual resistive switching, linear and symmetric conductance change, improved endurance and reproducibility characteristics compared to a single HfO2 device. Finally, we emulated spike-timing-dependent plasticity based learning rule with pulses inspired by neural action potential, indicating its potential as an electronic synaptic device in a hardware neuromorphic system.

Investigation of the multilevel capability of TiN/Ti/HfO2/W resistive switching devices by sweep and pulse programming

Multilevel states are clearly distinguished in TiN/Ti/HfO2/W RRAM devices by programming sequential voltage ramps and trains of pulses. It has been shown that the filamentary conductance has a continuous dependence on the current compliance employed during the SET process and the maximum voltage applied during the RESET process. The LRS conductance is determined by the current compliance, while the HRS is given by the RESET pulse amplitude. The obtained results suggest that the studied devices can be employed as electronic synaptic devices in neuromorphic circuits.

Effect of Ag doping on hydrothermally grown ZnO thin-film electronic synapse device

The present paper reports the effect of silver (Ag) doping on hydrothermally grown zinc oxide (ZnO) thin-film memristor-based electronic synapse devices. The morphological, structural and electrical characterizations of the zinc oxide memristor devices were carried out using scanning electron microscopy, X-ray diffraction and a programmable electrochemical workstation, respectively. The crystallographic orientation was detected along the (002) plane with well-aligned and compact zinc oxide nanorods for silver-doped zinc oxide memristor devices. An analog memory with a synaptic weight behavior similar to that of a biological synapse was observed for the developed devices. The morphological, structural, electrical and resistive switching mechanism studies clearly show improved performance of silver-doped zinc oxide devices over that of the bare zinc oxide device. Furthermore, the conduction mechanism of the devices follows the ohmic and space-charge-limited conduction theories. The present report is useful for the development and optimization of zinc oxide-based electronic synaptic devices for neuromorphic applications.

Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices

New parallel computing architectures based on neuromorphic computing are needed due to their advantages over conventional computation with regards to real‐time processing of unstructured sensory data such as image, video, or voice. However, developing artificial neuromorphic system remains a challenge due to the lack of electronic synaptic devices, which can mimic all the functions of biological synapses with low energy consumption. Here it is reported that two‐terminal organometal trihalide perovskite (OTP) synaptic devices can mimic the neuromorphic learning and remembering process. Various functions known in biological synapses are demonstrated in OTP synaptic devices including four forms of spike‐timing‐dependent plasticity (STDP), spike‐rate‐dependent plasticity (SRDP), short‐term plasticity (STP) and long‐term potentiation (LTP)), and learning‐experience behavior. The excellent photovoltaic property of the OTP devices also enables photo‐read synaptic functions. The perovskite synapse has the potential of low energy consumption of femto‐Joule/(100 nm)2 per event, which is close to the energy consumption of biological synapses. The demonstration of energy‐efficient OTP synaptic devices opens a new plausible application of OTP materials into neuromorphic devices, which offer the high connectivity and high density required for biomimic computing.

Investigation of the ferroelectric switching behavior of P(VDF-TrFE)-PMMA blended films for synaptic device applications

Synaptic plasticity can be mimicked by electronic synaptic devices. By using ferroelectric thin films as gate insulator for thin-film transistors (TFT), channel conductance can be defined as the synaptic plasticity, and gradually modulated by the variations in amounts of aligned ferroelectric dipoles. Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)]-poly(methyl methacrylate) (PMMA) blended films are chosen and their switching kinetics are investigated by using the Kolmogorov-Avrami-Ishibashi model. The switching time for ferroelectric polarization is sensitively influenced by the amplitude of applied electric field and volumetric ratio of ferroelectric beta-phases in the P(VDF-TrFE)-PMMA films. The switching time of the P(VDF-TrFE) increases with decreasing the pulse amplitude and/or the ratio of ferroelectric beta-phases by incorporation of PMMA. The activation electric field is also found to increase as the increase in blended amount of PMMA. Synapse TFTs are fabricated using the P(VDF-TrFE)-PMMA as gate insulator and In-Ga-Zn-O active channels. The drain currents of the synapse TFTs gradually increased when the voltage pulse signals with given duration are repeatedly applied. This suggests that the synaptic weights can be modulated by the number of external pulse signals, and that the proposed synapse TFT can be applied for mimicking the operations of bio-synapses.

A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.