Advanced 3-D synaptic devices with a stackable AND-type rounded dual channel (RDC) flash memory structure are proposed for neuromorphic networks. AND synaptic arrays composed of RDC flash devices enable program/erase (PGM/ERS) using Fowler–Nordheim (FN) tunneling, high-speed operation because of parallel read operations, and high density with multilayer stacking. Key fabrication steps are explained and the successful operation of the device in 3-D stacked structure is verified by measurement results. In addition, current summation and selective PGM/ERS behavior in synaptic arrays, which are essential in neuromorphic networks, are demonstrated. A hardware-based convolutional neural network (CNN) is designed considering the operating characteristics of the RDC flash memory. The accuracy evaluation and analysis for the CIFAR-10 image classification are performed. In addition, we propose a method of constructing a hardware-based CNN with the high-density synaptic array by stacking layers.
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