Herein, we propose an organic double heterojunction to enable a nonvolatile step modulation of the conductance of an artificial synapse; the double heterojunction is composed of N,N'-dioctyl-3,4,9,10-perylene tetracarboxylic diimide (PTCDI-C8), copper phthalocyanine (CuPc), and para-sexiphenyl (p-6P). The carrier confinement in the CuPc region present in the double-heterojunction structure enabled the nonvolatile modulation of the postsynaptic current. The proposed organic synapse exhibited an excellent conductance change, characteristic with a nonlinearity (NL) value below 0.01 in the long-term potentiation (LTP) region. Furthermore, the NL value for long-term depression (LTD) could be reduced effectively from 45 to 3.5 by a pulse modulation technique. A simple artificial neural network (ANN) was theoretically designed using the LTP/LTD characteristic curves of such organic synapses, and then, learning and recognition tasks were performed using Modified National Institute of Standards and Technology digit images. A four-amplitude weight update method enabled considerable enhancement of the recognition rate from 53 to 70%. Although the designed ANN was based on a single-layer perceptron model, a high maximum accuracy of 75% was achieved. These newly studied techniques for synaptic devices are expected to open up new possibilities for the realization of artificial synapses based on organic double heterojunctions.
Keywords: artificial synapse; band engineering; neuromorphic computing; organic heterojunction; pattern recognition.