Oxide semiconductors have been investigated as channel layers for thin film transistors (TFTs) which enable next-generation devices such as high-resolution liquid crystal displays (LCDs), organic light emitting diode (OLED) displays, flexible electronics, and innovative devices. Here, high-performance and stable Ga-Sn-O (GTO) TFTs were demonstrated for the first time without the use of rare metals such as In. The GTO thin films were deposited using radiofrequency (RF) magnetron sputtering. A high field effect mobility of 25.6 cm2/Vs was achieved, because the orbital structure of Sn was similar to that of In. The stability of the GTO TFTs was examined under bias, temperature, and light illumination conditions. The electrical behaviour of the GTO TFTs was more stable than that of In-Ga-Zn-O (IGZO) TFTs, which was attributed to the elimination of weak Zn-O bonds.

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Artificial intelligences are indispensable in future societies, and neural networks are representative that mimic biological brains. However, the conventional ones are complicated software on high-spec hardware, the machine size is bulky, and power consumption is huge. Neuromorphic systems are practical solutions composed solely of optimized hardware. Therefore, we are investigating neuromorphic systems with amorphous met-al-oxide-semiconductor thin-film devices as synapse elements and proposing modified Hebbian learning done locally without extra control circuits. As a result, the conductance deterioration can be utilized as synaptic plasticity. It is expected that the neuromorphic systems are three-dimensional-integrated systems, the size can be compact, power can be low, and all functions of biological brains are realized. In this study, we have developed neuromorphic systems with crosspoint-type amorphous Ga-Sn-O thin-film devices as self-plastic synapse elements, and fundamental operations are confirmed. First, crosspoint-type devices are fabricated, and it is found that the electric current gradually decreases along the bias time. Next, a neuromorphic system is actually implemented using a field-programmable-gate-array chip and crosspoint-type devices, and it is confirmed that a function of letter recognition is obtained after learning process. Once the fundamental operations are confirmed, more advanced functions will be obtained by increasing the device and circuit scales.

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