摘要： Arti?cial intelligences are essential concepts in smart societies, and neural networks are typical schemes that imitate human brains. However, the neural networks are conventionally realized using complicated software and high-performance hardware, and the machine size and power consumption are huge. On the other hand, neuromorphic systems are composed solely of optimized hardware, and the machine size and power consumption can be reduced. Therefore, we are investigating neuromorphic systems especially with amorphous metal-oxide semiconductor (AOS) thin-?lm devices. In this study, we have developed a Hop?eld neural network with double-layer AOS thin-?lm devices as crosspoint-type synapse elements. Here, we propose modi?ed Hebbian learning done locally without extra control circuits, where the conductance deterioration of the crosspoint-type synapse elements can be employed as synaptic plasticity. In order to validate the fundamental operation of the neuromorphic system, ?rst, double-layer AOS thin-?lm devices as crosspoint-type synapse elements are actually fabricated, and it is found that the electric current continuously decreases along the bias time. Next, a Hop?eld neural network is really assembled using a ?eld-programmable gate array (FPGA) chip and the double-layer AOS thin-?lm devices, and it is con?rmed that a necessary function of the letter recognition is obtained after learning process. Once the fundamental operations are con?rmed, more advanced functions will be obtained by scaling up the devices and circuits. Therefore, it is expected the neuromorphic systems can be three-dimensional (3D) large-scale integration (LSI) chip, the machine size can be compact, power consumption can be low, and various functions of human brains will be obtained. What has been developed in this study will be the sole solution to realize them.