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The development of a reliable optoelectronic memristor is crucial for the success of neuromorphic vision systems, but current devices are limited by stochastic resistance switching and uncontrollable ion dynamics. To address these challenges, a core–shell nanosphere composite was synthesized using CsPbBr3 quantum dots and block copolymer polystyrene-poly2-vinyl pyridine. This memristor exhibits both negative differential resistance and resistive switching memory behavior and has demonstrated exceptional stability, withstanding over 5000 cycles and remaining stable for over 5 million seconds. It is also tunable by light irradiation, making it ideal for optoelectronic logic operations and multi-derivative state applications. The memory behavior is attributed to an electrochemical metalization mechanism involving the formation and annihilation of pyridine groups, conductive metal filaments, and bromide ion vacancies in S2VP filaments. This work presents an effective method for fabricating robust multi-derivative perovskite optoelectronic devices for neuromorphic computing systems. Additionally, machine learning algorithms have demonstrated accurate digital image classification and recognition using photoelectric pulse-driven neuromorphic computation with an accuracy of over 97%. [ABSTRACT FROM AUTHOR]

According to Moore's Law's development law, traditional floating gate memory is constrained by charge tunneling, and its size is approaching the physical limit, which is insufficient to meet the requirements of large data storage. The introduction of new information storage devices may be the key to overcoming the bottleneck. Resistive random access memory (RRAM) has garnered interest due to its fast switching speed, low power consumption, and high integration density. The resistive switching (RS) behaviors can be demonstrated in many materials, including transition metal oxides, perovskite oxides and organic matter, etc. Among these materials, graphene oxide (GO) with its unique physical, chemical properties and excellent mechanical properties is attracting significant attention for use in RRAM owing to its RS operation and potential for integration with other graphene-based electronics. However, there is unacceptable variability in RS reliability, including retention and endurance, which is the key factor that affects the development of memristors. In addition, the RS mechanism of GO-based RRAM has not been systematically discussed. In this article, we discuss systematically several typical models of the switching mechanism of GO-based RRAM and a summary of methods for improving the device's RS performance. This article concludes by discussing the applications of GO-RRAM in artificial neural networks, flexible devices, and biological monitoring. [ABSTRACT FROM AUTHOR]

Current resistive access memories (RRAM) suffer from severe resistive switching variability issues due to the large stochasticity in the formation and disruption process of conductive filaments. Here, a material strategy is applied in designing an RRAM device that can substantially improve the switching uniformity by utilizing a silver imidazole complex as the switching medium. In the silver imidazole, Ag species has a moderate reduction Gibbs energy which can participate in the redox reaction to change the resistance state of the device. Moreover, as a resistive layer, the migration range of silver ions is limited in the grain of silver imidazole, which shortens the migration distance of silver ions and reduces the randomness caused by silver ion migration, thus improving the uniformity and stability of the device. Through this facile material engineering strategy, the RRAM exhibits enhanced performance with low spatial variation of 7.86%, low temporal variation of 1.78% and low operation voltage of 0.50 V. The high switching uniformity of silver imidazole RRAM allows us to employ them as building blocks for logic gates with high yield. The self‐reduction and grain boundary confinement effect of switching materials for RRAM design may open the way to the development of large‐scale circuits for non‐volatile computing and machine learning. [ABSTRACT FROM AUTHOR]

As one key issue of resistive switching (RS) memory, the cycling endurance is poorly understood in hybrid perovskite‐based memory devices. Here, the cycling failure and the corresponding cycling‐induced degradation of CH3NH3PbI3‐based resistive random access memory devices are discussed. The high resistance state clearly decreases with the number of operation cycles, finally triggering irreversible failure in the collapse of switching window. By monitoring the I–V curves for all cycles, a negative set event is observed to be the critical turning point that considerably accelerates the cycling degradation rate. The decrease of |Vset| and |Vreset| indicates a reduction of the migration barrier of iodine vacancies (VI), which accounts for the appearance of a negative set after cycling. The understanding of RS cycling degradation can promote the optimization of device endurance by slowing the defect accumulation rate. The endurance degradation of CH3NH3PbI3‐based RRAM is attributed to the excessive defect accumulation during cycling operation. During the reset process, a negative set event is observed to accelerate the endurance degradation rate. Moreover, the decrease of transition voltages suggests a reduction of the migration barrier of iodine vacancies with cycling operation. [ABSTRACT FROM AUTHOR]