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This study systematically investigates analog switching and neuromorphic characteristics in a ZnO-based memristor by varying the anodic top electrode (TE) materials [indium tin oxide (ITO), Ti, and Ta]. Compared with the TE materials (Ti and Ta), memristive devices with TEs made of ITO exhibit dual volatile and nonvolatile switching behavior and multistate switching characteristics assessed based on reset-stop voltage and current compliance (ICC) responses. The polycrystalline structure of the ZnO functional layer sandwiched between ITO electrodes was confirmed by high-resolution transmission electron microscopy analysis. The current transport mechanism in the ZnO-based memristor was dominated by Schottky emission, with the Schottky barrier height modulated from 0.26 to 0.4 V by varying the reset-stop voltage under different ICC conditions. The long-term potentiation and long-term depression synaptic characteristics were successfully mimicked by modulating the pulse amplitudes. Furthermore, a 90.84% accuracy was achieved using a convolutional neural network architecture for Modified National Institute of Standards and Technology pattern categorization, as demonstrated by the confusion matrix. The results demonstrated that the ITO/ZnO/ITO/Si memristor device holds promise for high-performance electronic applications and effective ITO electrode modeling. [ABSTRACT FROM AUTHOR]

Efficient data processing is heavily reliant on prioritizing specific stimuli and categorizing incoming information. Within human biological systems, dorsal root ganglions (particularly nociceptors situated in the skin) perform a pivotal role in detecting external stimuli. These neurons send warnings to our brain, priming it to anticipate potential harm and prevent injury. In this study, we explore the potential of using a ferroelectric memristor device structured as a metal–ferroelectric–insulator–semiconductor as an artificial nociceptor. The aim of this device is to electrically receive external damage and interpret signals of danger. The TiN/HfAlOx (HAO)/HfSiOx (HSO)/n+ Si configuration of this device replicates the key functions of a biological nociceptor. The emulation includes crucial aspects, such as threshold reactivity, relaxation, no adaptation, and sensitization phenomena known as "allodynia" and "hyperalgesia." Moreover, we propose establishing a connection between nociceptors and synapses by training the Hebbian learning rule. This involves exposing the device to injurious stimuli and using this experience to enhance its responsiveness, replicating synaptic plasticity. [ABSTRACT FROM AUTHOR]

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations. [ABSTRACT FROM AUTHOR]