Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaO_x based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaO_x interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.

Memory cells have always been an important element of information technology. With emerging technologies like big data and cloud computing, the scale and complexity of data storage has reached an unprecedented peak with a much higher requirement for memory technology. As is well known, better data storage is mostly achieved by miniaturization. However, as the size of the memory device is reduced, a series of problems, such as drain gate‐induced leakage, greatly hinder the performance of memory units. To meet the increasing demands of information technology, novel and high‐performance memory is urgently needed. Fortunately, emerging memory technologies are expected to improve memory performance and drive the information revolution. This review will focus on the progress of several emerging memory technologies, including two‐dimensional material‐based memories, resistance random access memory (RRAM), magnetic random access memory (MRAM), and phase‐change random access memory (PCRAM). Advantages, mechanisms, and applications of these diverse memory technologies will be discussed in this review. image

In this letter, the impacts introduced by oxygen-scavenging metal (Ti), including forming voltages variation and resistances dispersion in high/low states of HfOx-based RRAM devices, were investigated. Based on experiments, it is concluded that Ti atoms during physical vapor deposition process can randomly penetrate into the bulk of HfOx and are intended to randomly create the initial oxygen vacancies, thus causing the performance degradation. Moreover, a novel structure with an atomic layer deposition-TiN buffer layer inserted between oxygen-scavenging Ti and HfOx layers was proposed and experimentally demonstrated to reduce the impacts of Ti penetration, which further improves vacancy-creating approach. It was experimentally verified that with the help of incorporating a TiN buffer layer, the uniformity of switching parameters, such as forming voltage and resistance, was effectively improved. The understanding and improvement of device variation caused by oxygen-scavenging metal in oxides-based resistive random access memory (RRAM) are useful for further development of highly reliable RRAM technology.

We propose an electronic synapse based on an Al: HfO2 metal-ferroelectric metal (MFM) capacitor with multi-level characteristics. The device demonstrates excellent multilevel behavior, linearity, weight update symmetry, and low static power consumption. Further, an artificial neural network based on this capacitor is built and evaluated through SPICE and MATLAB simulations. The performance indicates that higher than 78% accuracy and nJ/ms dynamic speed-energy efficiency in recognizing the standard MNIST handwritten digits are achieved, showing the capability of high-density integration and low energy consumption of capacitive artificial neural networks.

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.