Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix

Quantum dot-based memristors for information processing and artificial intelligence applications

Traditional computing systems struggle to keep pace with the development of artificial intelligence, as well as the development of the economy and continuous innovation in science and technology. Therefore, there is an urgent need for a new generation of powerful yet low-power computing technologies to replace them. Quantum dots have been incorporated into memristors due to their unique electrical properties, and the development of quantum dot memristors is expected to solve the problems faced by traditional memristors, including cycle stability, high energy consumption, and conductivity uniformity. This article reviews the research progress of quantum dot memristors and their simulation applications in artificial synapses. It summarizes some of the current challenges faced in the development of quantum dot memristors and discusses the potential future applications of these memristors in the field of artificial intelligence.

Proton-electron coupling and mixed conductivity in a hydrogen-bonded coordination polymer

The fundamental understanding of coupled proton-electron transport in mixed protonic-electronic conductors (MPECs) remains unexplored in materials science, despite its potential significance within the broader context of mixed ionic-electronic conductors (MIECs) and the possibility of controlled diffusion of protons using hydrogen-bond networks. To address these limitations, we present a hydrogen-bonded coordination polymer Ni-BAND ({[Ni(bpy)(H2O)2(DMF)2](NO3)2·2DMF}n), which demonstrates high mixed protonic-electronic conductivity at room temperature. Through detailed analysis, we unravel the coupled transport mechanism, offering insights for the rational design of high-performance MPECs. We demonstrate the practical implications of this mechanism by examining the humidity-dependent synaptic plasticity of Ni-BAND, showcasing how MPECs can expand into traditional MIEC applications while leveraging their unique proton-mediated advantages.

All-Inorganic CsPbBr3 Perovskite Planar-Type Memristors as Optoelectronic Synapses

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr3 single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr3 single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability

During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).

MoS2 Quantum Dot-Optimized Conductive Channels for a Conjugated Polymer-Based Synaptic Memristor

Donor-acceptor-type conjugated polymers are widely used in memristors due to their unique push-pull electron structures and charge transfer mechanisms. However, the inherently inhomogeneous microstructure of polymer films and their low crystallinity produce randomness that destabilizes formed conductive channels, giving polymer-based memristors unstable switching behavior. In this contribution, we prepared a synaptic device based on PM6-MoS2 QD (molybdenum disulfide quantum dot) nanocomposites. In the composites, MoS2 QDs provided the active centers for forming conductive channels via electron trapping and detrapping. They also controlled the directional formation of conductive channels between PM6 and MoS2 QDs, reducing randomness and giving devices a narrow switching voltage range and cycling longevity. The device exhibited continuous multistage conductance states under a direct current voltage sweep and simulated a variety of synaptic functions, including long-term potentiation, long-term depression, short-term potentiation, short-term depression, paired-pulse facilitation, spiking-rate-dependent plasticity, and "learning experience" behavior. The memristor could also perform arithmetic, including "counting" and "subtraction" operations. This work provides a new approach to improving the performance of memristors for neuromorphic computing.