RÉSUMÉ
Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.
RÉSUMÉ
Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.
RÉSUMÉ
The conventional von Neumann architecture has proven to be inadequate in keeping up with the rapid progress in artificial intelligence. Memristors have become the favored devices for simulating synaptic behavior and enabling neuromorphic computations to address challenges. An artificial synapse utilizing the perovskite structure PbHfO3 (PHO) has been created to tackle these concerns. By employing the sol-gel technique, a ferroelectric film composed of Au/PHO/FTO was created on FTO/glass for the purpose of this endeavor. The artificial synapse is composed of Au/PHO/FTO and exhibits learning and memory characteristics that are similar to those observed in biological neurons. The recognition accuracy for both MNIST and Fashion-MNIST data sets saw an increase, reaching 92.93% and 76.75%, respectively. This enhancement resulted from employing a convolutional neural network architecture and implementing an improved stochastic adaptive algorithm. The presented findings showcase a viable approach to achieve neuromorphic computation by employing artificial synapses fabricated with PHO.