Neuromorphic computing represents an innovative technology that can perform intelligent and energy-efficient computation, whereas construction of neuromorphic systems requires biorealistic synaptic elements with rich dynamics that can be tuned based on a robust mechanism. Here, an ionic-gating-modulated synaptic transistor based on layered crystals of transitional metal dichalcogenides and phosphorus trichalcogenides is demonstrated, which produce a diversity of short-term and long-term plasticity including excitatory postsynaptic current, paired pulse facilitation, spiking-rate-dependent plasticity, dynamic filtering, etc., with remarkable linearity and ultralow energy consumption of ≈30 fJ per spike. Detailed transmission electron microscopy characterization and ab initio calculation reveal two-stage ionic gating effects, namely, surface adsorption and internal intercalation in the channel medium, causing different poststimulation diffusive dynamics and thus accounting for the observed short-term and long-term plasticity, respectively. The synaptic activity can therefore be effectively manipulated by tailoring the ionic gating and consequent diffusion dynamics with varied thickness and structure of the van der Waals material as well as the number, duration, rate, and polarity of gate stimulations, making the present synaptic transistors intriguing candidates for low-power neuromorphic systems.

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 TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx 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.