Nonideal resistive and synaptic characteristics in Ag/ZnO/TiN device for neuromorphic system

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision

Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event-triggered spiking, has higher efficiency and lower energy consumption. Here, this work proposes an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self-powered and event-triggered, outputting light-modulated spiking signals directly, thereby fulfilling the imperative for always-on with low-power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere five epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always-on low-power artificial vision system.

Impact of Top Electrodes (Cu, Ag, and Al) on Resistive Switching behaviour of Cu-rich Cu2 ZnSnS4 (CZTS) Ideal Kesterite

Cu2 ZnSnS4 (CZTS) active material-based resistive random-access memory (RRAM) devices are investigated to understand the impact of three different Cu, Ag, and Al top electrodes. The dual resistance switching (RS) behaviour of spin coated CZTS on ITO/Glass is investigated up to 102 cycles. The stability of all the devices (Cu/CZTS/ITO, Ag/CZTS/ITO, and Al/CZTS/ITO) is investigated up to 103  sec in low- (LRS) and high- (HRS) resistance states at 0.2 V read voltage. The endurance up to 102 cycles with 30 msec switching width shows stable write and erase current. Weibull cumulative distribution plots suggest that Ag top electrode is relatively more stable for set and reset state with 33.61 and 25.02 shape factors, respectively. The charge carrier transportation is explained by double logarithmic plots, Schottky emission plots, and band diagrams, substantiating that at lower applied electric field intrinsic copper ions dominate in Cu/CZTS/ITO, whereas, at higher electric filed, top electrodes (Cu and Ag) dominate over intrinsic copper ions. Intrinsic Cu+ in CZTS plays a decisive role in resistive switching with Al electrode. Further, the impedance spectroscopy measurements suggest that Cu+ and Ag+ diffusion is the main source for the resistive switching with Cu and Ag electrodes.

Self-Assembled Lanthanum Oxide Nanoflakes by Electrodeposition Technique for Resistive Switching Memory and Artificial Synaptic Devices

In recent years, many metal oxides have been rigorously studied to be employed as solid electrolytes for resistive switching (RS) devices. Among these solid electrolytes, lanthanum oxide (La2 O3 ) is comparatively less explored for RS applications. Given this, the present work focuses on the electrodeposition of La2 O3 switching layers and the investigation of their RS properties for memory and neuromorphic computing applications. Initially, the electrodeposited La2 O3 switching layers are thoroughly characterized by various analytical techniques. The electrochemical impedance spectroscopy (EIS) and Mott-Schottky techniques are probed to understand the in situ electrodeposition, RS mechanism, and n-type semiconducting nature of the fabricated La2 O3 switching layers. All the fabricated devices exhibit bipolar RS characteristics with excellent endurance and stable retention. Moreover, the device mimics the various bio-synaptic properties such as potentiation-depression, excitatory post-synaptic currents, and paired-pulse facilitation. It is demonstrated that the fabricated devices are non-ideal memristors based on double-valued charge-flux characteristics. The switching variation of the device is studied using the Weibull distribution technique and modeled and predicted by the time series analysis technique. Based on electrical and EIS results, a possible filamentary-based RS mechanism is suggested. The present results assert that La2 O3 is a promising solid electrolyte for memory and brain-inspired applications.

The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfOx/W Bilayer-Structured Memory Device

This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments at the interface of ZnO and HfOx contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfOx layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfOx/W structure holds the potential to be a viable memory component for integration into neuromorphic systems.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices

As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO₂/TaO_x/TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO₂ layer. Moreover, Pt/HfO₂/TaO_x/TaN with a 3 nm thick HfO₂ layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO₂/TaO_x/TaN with Pt/TaO_x/TaN, which has insufficient performance to be used as a nociceptor.

Resistive Switching Characteristics of Alloyed AlSiOx Insulator for Neuromorphic Devices

Charge-based memories, such as NAND flash and dynamic random-access memory (DRAM), have reached scaling limits and various next-generation memories are being studied to overcome their issues. Resistive random-access memory (RRAM) has advantages in structural scalability and long retention characteristics, and thus has been studied as a next-generation memory application and neuromorphic system area. In this paper, AlSiO_x, which was used as an alloyed insulator, was used to secure stable switching. We demonstrate synaptic characteristics, as well as the basic resistive switching characteristics with multi-level cells (MLC) by applying the DC sweep and pulses. Conduction mechanism analysis for resistive switching characteristics was conducted to understand the resistive switching properties of the device. MLC, retention, and endurance are evaluated and potentiation/depression curves are mimicked for a neuromorphic device.

Multi-level Cells and Quantized Conductance Characteristics of Al2O3-Based RRAM Device for Neuromorphic System

Recently, various resistance-based memory devices are being studied to replace charge-based memory devices to satisfy high-performance memory requirements. Resistance random access memory (RRAM) shows superior performances such as fast switching speed, structural scalability, and long retention. This work presented the different filament control by the DC voltages and verified its characteristics as a synaptic device by pulse measurement. Firstly, two current-voltage (I-V) curves are characterized by controlling a range of DC voltages. The retention and endurance for each different I-V curve were measured to prove the reliability of the RRAM device. The detailed voltage manipulation confirmed the characteristics of multi-level cell (MLC) and conductance quantization. Lastly, synaptic functions such as potentiation and depression, paired-pulse depression, excitatory post-synaptic current, and spike-timing-dependent plasticity were verified. Collectively, we concluded that Pt/Al₂O₃/TaN is appropriate for the neuromorphic device.

Resistive Switching and Synaptic Characteristics in ZnO/TaON-Based RRAM for Neuromorphic System

We fabricated an ITO/ZnO/TaON/TaN device as nonvolatile memory (NVM) with resistive switching for complementary metal-oxide-semiconductor (CMOS) compatibility. It is appropriate for the age of big data, which demands high speed and capacity. We produced a TaON layer by depositing a ZnO layer on a TaN layer using an oxygen-reactive radio frequency (RF) sputtering system. The bi-layer formation of ZnO and TaON interferes with the filament rupture after the forming process and then raises the current level slightly. The current levels were divided into high- and low-compliance modes. The retention, endurance, and pulse conductance were verified with a neuromorphic device. This device was stable and less consumed when it was in low mode rather than high mode.

Demonstration of Threshold Switching and Bipolar Resistive Switching in Ag/SnOx/TiN Memory Device

In this work, we observed the duality of threshold switching and non-volatile memory switching of Ag/SnOx/TiN memory devices by controlling the compliance current (CC) or pulse amplitude. The insulator thickness and chemical analysis of the device stack were confirmed by transmission electron microscope (TEM) images of the Ag/SnOx/TiN stack and X-ray photoelectron spectroscopy (XPS) of the SnOx film. The threshold switching was achieved at low CC (50 μA), showing volatile resistive switching. Optimal CC (5 mA) for bipolar resistive switching conditions with a gradual transition was also found. An unstable low-resistance state (LRS) and negative-set behavior were observed at CCs of 1 mA and 30 mA, respectively. We also demonstrated the pulse operation for volatile switching, set, reset processes, and negative-set behaviors by controlling pulse amplitude and polarity. Finally, the potentiation and depression characteristics were mimicked by multiple pulses, and MNIST pattern recognition was calculated using a neural network, including the conductance update for a hardware-based neuromorphic system.