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We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

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A comprehensive numerical model for chalcogenide glasses is presented, coupling a physically based electrical model able to reproduce the threshold switching with a local nucleation and growth algorithm to account for the phase transition dynamics. The main ingredients of the chalcogenide physics are reviewed and analyzed through simplified analytical models, providing a deeper insight on the origin of the threshold switching mechanism in chalcogenide glasses. A semiconductorlike three-dimensional full-coupled numerical implementation of the proposed model is finally presented and its capabilities to quantitatively reproduce the key elements of the Ge2Sb2Te5 chalcogenide physics are demonstrated in the framework of phase change memory device simulations.

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