With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

New computing-in-memory architecture based on memristors can achieve in situ storage and computing of data, which greatly improves the computing efficiency of the hardware system. Here, a reliable bilayer structured TaO x /Al 2 O 3 memristor with a 2 nm Al 2 O 3 insertion layer is demonstrated. This device exhibits stable and gradual switching behavior with a low set/reset voltage (0.61 V/−0.49 V) and multilevel conductance characteristics. It is further indicated that the device has a larger ON/Off ratio (≈148×) and better nonlinearity of conductance modulation by inserting an Al 2 O 3 layer. Various forms of synaptic plasticity are mimicked, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP). Based on the quasi-linear conductance modulation characteristics, excellent classification accuracy (90.4%) is achieved for the applications of handwritten digit recognition. Moreover, the logic operations (intersection, union, and complement) are implemented on a 3 × 5 memristor array, which shows an efficient way to design versatile and reliable devices and provides a novel idea for neuromorphic computing and in-memory logic operation.

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Neuromorphic Computing In article number 2200086, Jun Zhang, Cong Ye, and co‐workers introduce a bilayer structured memristor based on TaOx/Al2O3. This device, which shows stable and gradual switching behavior, shows potential for use in neuromorphic computing and in‐memory logic operation. The latter application in particular could greatly improve the efficiency of novel computing systems. The latter application in particular could greatly improve the efficiency of novel computing systems. The cover image shows the implementation of the image logic “AND” function through a non‐volatile logic operation scheme.