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Neural networks are information processing models based on the human brain, and they have been activity studied. However, in order to realize the hardware of the neural network, it is necessary to achieve high integration. In this study, we fabricated synapses for neural networks using the Ga-Sn-O (GTO) thin films, which is rare-metal-free amorphous oxide semiconductor. The synapses are planar type,...
We have developed a planar device using In-Ga-Zn-O (IGZO) semiconductor for synapse element in neural network. First, we formed the planar device on a glass substrate. Next, we formed it on an LSI wafer. Both devices shows proper uniformities of film thicknesses and sufficient degradations of electric characteristics, which can be utilized for modified Hebbian learning proposed by us. These results...
Neural networks are computing models based on human brains. We propose a neural network using a field-programmable gate array (FPGA) for neurons and amorphous In-Ga-Zn-O (a-IGZO) thin films for synapses. It is found that electric current in the a-IGZO thin film gradually decreases along the time. On the other hand, the degradation does not occur when light is irradiated. These phenomena can be utilized...
We are developing neural networks using thin-film transistors (TFTs). By adopting an interconnect-type neural network and utilizing a characteristic degradation of poly-Si TFTs as a variable strength of synapse connection, which was originally an issue, we realized the neuron consisting of eight TFTs and synapse of only one TFT. Particularly in this presentation, we confirmed that the learning efficiency...
We are developing device-level neural networks using poly-Si TFTs. We succeeded in dramatically reducing the number of transistors in neurons and synapses to integrate a lot of devices, and we also succeeded in actually checking the operation of learning of logics. In this presentation, for the purpose of improvement of learning efficiency, we changed the synapse TFTs from the SD structure to the...
We have evaluated characteristic deviation and characteristic degradation of poly-Si thin-film phototransistors. We found that the characteristic deviation is not negligible, which seems due to energy distribution of excimer-laser crystallization, and must be compensated for some applications. We found that the characteristic degradation is negligible, which is convenient for abovementioned applications.
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