A Hybrid Memristor/CMOS SNN for Implementing One-Shot Winner-Takes-All Training

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This paper presents a spiking neural network for pattern recognition. The network synapses are realized by resistive switching random access memory (ReRAM) cells, which are a stack of Au/Ti/C/Ti/HfO2/Pt. These cells are connected to an array of NMOS transistors (fabricated in a CMOS 180nm technology) to form a 4by4 1T1R crossbar between pre and postsynaptic circuitries. The pre-synaptic part contains conditioning circuits to reshape the inputs before applying them to the memristive crossbar. The post-synaptic section includes current attenuators that allowed the memristor domain currents to be mapped to neuron domain currents, as well as physiologically realistic neuron circuits fabricated in a CMOS 180nm technology. As a demonstrator, the network is trained with one-shot winner-takes-all method to differentiate four input patterns in its inference mode.

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