Modulation of nonlinear resistive switching behavior of a TaOx-based resistive device through interface engineering.

A resistive switching device with inherent nonlinear characteristics through a delicately engineered interfacial layer is an ideal component to be integrated into passive crossbar arrays for the suppression of sneaking current, especially in ultra-dense 3D integration. In this paper, we demonstrated a TaOx-based bipolar resistive switching device with a nearly symmetrical bi-directional nonlinear feature through interface engineering. This was accomplished by introducing an ultra-thin interfacial layer (SiO2-x) with unique features, including a large band gap and a certain level of negative heat of oxide formation between the top electrode (TiN) and resistive layer (TaOx). The devices exhibit excellent nonlinear property under both positive and negative bias. Modulation of the inherent nonlinearity as well as the resistive switching mechanism are comprehensively studied by scrutinizing the results of the experimental control groups and the extensive characterizations including detailed compositional analysis, which suggests that the underlying mechanism of the nonlinear behavior is associatively governed by the serially connected metallic conductive filament and Flower-Nordheim tunneling barrier formed by the SiO2-x interface layer. The proposed device in this work has great potential to be implemented in future massive storage memory applications of high-density selector-free crossbar structure.

Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing.

Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.

Multi-terminal ionic-gated low-power silicon nanowire synaptic transistors with dendritic functions for neuromorphic systems.

Neuromorphic computing systems have shown powerful capability in tasks, such as recognition, learning, classification and decision-making, which are both challenging and inefficient in using the traditional computation architecture. The key elements including synapses and neurons, and their feasible hardware implementation are essential for practical neuromorphic computing. However, most existing synaptic devices used to emulate functions of a single synapse and the synapse-based networks are more energy intensive and less sustainable than their biological counterparts. The dendritic functions such as integration of spatiotemporal signals and spike-frequency coding characteristics have not been well implemented in a single synaptic device and thus play an imperative role in future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors are fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Herein, we demonstrate a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with low power consumption (<400 fJ per switching event) based on the standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as "Add" and "Subtraction") could be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including the short-term and long-term synaptic plasticity, paired pulse facilitation and high-pass filtering were also successfully demonstrated based on 40 nm wide IGNWFETs. The migration of ions in polymer electrolyte and trapping in high-k dielectric were also experimentally studied in-depth to understand the short-term plasticity and long-term plasticity. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFET demonstrates its potential application in future biologically emulated neuromorphic systems.

Dual-Gated MoS2 Neuristor for Neuromorphic Computing.

The field of neuromorphic computing systems has been through enormous progress in recent years, whereas some issues are still remaining to be solved. One of the biggest challenges in neuromorphic circuit designing is the lack of a robust device with functions comparable to or even better than the metal-oxide-semiconductor field-effect transistor (MOSFET) used in traditional integrated circuits. In this work, we demonstrated a MoS2 neuristor using a dual-gate transistor structure. An ionic top gate is designed to control the migration of ions, while an electronic back gate is used to control electronic migration. By applying different driving signals, the MoS2 neuristor can be programmed as a neuron, a synapse, or an n-type MOSFET, which can be seen as a fundamental building block in the neuromorphic circuit design. The MoS2 neuristor provides viable solutions for future reconfigurable neuromorphic systems and can be a promising candidate for future neuromorphic computing.

Artificial Shape Perception Retina Network Based on Tunable Memristive Neurons.

Retina shows an extremely high signal processing efficiency because of its specific signal processing strategy which called computing in sensor. In retina, photoreceptor cells encode light signals into spikes and ganglion cells finish the shape perception process. In order to realize the neuromorphic vision sensor, the one-transistor-one-memristor (1T1M) structure which formed by one memristor and one MOSFET in serial is used to construct photoreceptor cell and ganglion cell. The voltage changes between two terminals of memristor and MOSFET can mimic the changes of membrane potential caused by spikes and illumination respectively. In this paper, the tunable memristive neurons with 1T1M structures are built. According to the concept of receptive field of ganglion cells (GCs) in the retina, the artificial shape perception retina network is constructed with these memristive neurons. The final results show that the artificial retina can extract shape information from the image and transfer it into spike frequency realizing the function of computing in sensor.

Novel Vertical 3D Structure of TaOx-based RRAM with Self-localized Switching Region by Sidewall Electrode Oxidation.

A novel vertical 3D RRAM structure with greatly improved reliability behavior is proposed and experimentally demonstrated through basically compatible process featuring self-localized switching region by sidewall electrode oxidation. Compared with the conventional structure, due to the effective confinement of the switching region, the newly-proposed structure shows about two orders higher endurance (>10(8) without verification operation) and better retention (>180h@150 °C), as well as high uniformity. Corresponding model is put forward, on the base of which thorough theoretical analysis and calculations are conducted as well, demonstrating that, resulting from the physically-isolated switching from neighboring cells, the proposed structure exhibits dramatically improved reliability due to effective suppression of thermal effects and oxygen vacancies diffusion interference, indicating that this novel structure is very promising for future high density 3D RRAM application.

Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device.

Animals' survival is dependent on their abilities to adapt to the changing environment by adjusting their behaviours, which is related to the ubiquitous learning behaviour, nonassociative learning. Thus mimicking the indispensable learning behaviour in organisms based on electronic devices is vital to better achieve artificial neural networks and neuromorphic computing. Here a three terminal device consisting of an oxide-based memristor and a NMOS transistor is proposed. The memristor with gradual conductance tuning inherently functions as the synapse between sensor neurons and motor neurons and presents adjustable synaptic plasticity, while the NMOS transistor attached to the memristor is utilized to mimic the modulatory effect of the neuromodulator released by inter neurons. Such a memristor-based multi-terminal device allows the practical implementation of significant nonassociative learning based on a single electronic device. In this study, the experience-induced modification behaviour, both habituation and sensitization, was successfully achieved. The dependence of the nonassociative behavioural response on the strength and interval of presented stimuli was also discussed. The implementation of nonassociative learning offers feasible and experimental advantages for further study on neuromorphic systems based on electronic devices.