Titanium oxide-based optoelectronic synapses with visual memory synergistically adjusted by internal emotions and ambient illumination.

Brain-inspired neuromorphic computing has become one of the critical technologies to overcome the bottleneck of von Neumann architecture. It is a vital step to construct a brain-like neuromorphic computing system at the hardware level by utilizing artificial synaptic devices. Compared with electronic synaptic devices, optoelectronic synaptic devices have the advantages of low power consumption, low crosstalk, and high bandwidth. Artificial optoelectronic synapses, analogous to retinal structure, can directly respond to and process light signal information to mimic the neuromorphic visual system. As high-level nerve impulses, both generated and regulated, emotions affect the strength and persistence of memory. Ambient illumination can provide visual perception to distinguish the size, color, and other characteristics of objects as well as affect the nonvisual functions of individuals, such as emotional states, thereby affecting learning and memory function. Herein, an artificial optoelectronic synapse composed of ITO/TiO2-x /p-Si was proposed. A variety of biologically dependent synaptic plasticity relating to learning and memory, including short-term synaptic plasticity, long-term synaptic plasticity, and learning-forgetting-relearning multifunctional advanced synaptic activity, was successfully simulated. A 3 × 3 artificial optoelectronic synapse array based on 9 devices was constructed to mimic the functions of visual learning and memory affected by internal emotion and ambient illumination. The proposed artificial optoelectronic synapse will exhibit great potential in visual and image information perception and memory.

A univariate ternary logic and three-valued multiplier implemented in a nano-columnar crystalline zinc oxide memristor.

Memristors, which feature small sizes, fast speeds, low power, CMOS compatibility and nonvolatile modulation of device resistance, are promising candidates for next-generation data storage and in-memory computing paradigms. Compared to the binary logics enabled by memristor devices, ternary logics with larger information-carrying capacity can provide higher computation efficiency with simple operation schemes, reduced circuit complexity and smaller chip areas. In this study, we report the fabrication of memristor devices based on nano-columnar crystalline ZnO thin films; they show symmetric and reliable multi-level resistive switching characteristics over three hundred cycles, which benefits the implementation of univariate ternary logic operations. Experimental results demonstrate that a three-valued logic complete set can be realized by the univariate operations of the present ZnO memristor device, and a ternary multiplier unit circuit is designed for potential applications. The present methodology can be beneficial for constructing future high-performance computation architectures.