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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,...
Nuromorphic devices are powerful information processing systems and realize low power consumption. We proposed cellular neural networks with polycrystalline-silicon (Poly-Si) thin film transistors (TFTs). The neuron element consists of eight TFTs and the synapse element of only one TFT. We actually succeeded in confirming the operation of learning of logics. In this presentation, we succeeded the...
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...
We have developed a cross-point device using In-Ga-Zn-O (IGZO) semiconductor for synapse element in neural network. Horizontal 80 and vertical 80 metal lines make 6400 cross-point synapse integrated on a glass substrate. The electrical conductance gradually degrades by flowing current, which is available for modified Hebbian learning.
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 cellular neural networks using thin-film transistors (TFTs). Although simplification of the processing elements such as neurons and synapses is also needed for the cellular neural network, it is difficult and time-consuming to fabricate and evaluate actual devices. Therefore, we are studying the simplification of the processing elements in the neural networks by using circuit simulation...
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...
Artificial neural networks are promising systems for information processing with many advantages, such as self-teaching and parallel distributed computing. However, conventional ones consist of extremely intricate circuits to guarantee accurate behaviors of the neurons and synapses. We demonstrate an apoptotic self-organized electronic device using thin-film transistors for artificial neural networks...
We are developing neural networks of device level using thin-film transistors (TFT). By adopting an interconnect-type neural network and utilizing a characteristic shift 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 the working...
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