We present an original method to implement neuro-inspired supervised learning for a synaptic array based on carbon nanotube devices. The device characteristics required to implement on chip learning within a crossbar of carbon nanotube field effect transistors (CNTFETs) as synaptic arrays were experimentally demonstrated and accurately modeled through a specific electrical compact model. We performed electrical simulations of learning for an array of 24 nanotube memory devices corresponding to a 3 input × 3 output neural layer that revealed successful learning of separable logic functions within very few epochs, even when a realistic variability of nanotube diameter was taken into account. Such a learning approach opens the way to the use of high-density synaptic arrays as generic logic blocks in configurable circuits.
['Si-Yu Liao', 'Jean-Marie Retrouvey', 'G. Agnus', 'Weisheng Zhao', 'Cristell Maneux', 'Sebastien Fregonese', 'Thomas Zimmer', 'Djaafar Chabi', 'Arianna Filoramo', 'Vincent Derycke', 'Christian Gamrat', 'Jacques-Olivier Klein']
Design and Modeling of a Neuro-Inspired Learning Circuit Using Nanotube-Based Memory Devices