Hardware artificial neural network (ANN) system with high density synapse devices can perform massive parallel computing for pattern recognition with low power consumption. To implement neuromorphic system with on-chip learning capability, we need to develop ideal synapse device with various device requirements such as scalability, MLC characteristics, low power operation, data retention, and symmetric and linear conductance change under potentiation/depression modes. Although various devices such as ReRAM, PRAM, and MRAM were proposed for synapse applications, these devices have limitation for neuromorphic system application.
In this talk, I will cover various ReRAM synapse devices such as filamentary switching ReRAM (HfOx, TaOx, Cu-CBRAM) with MLC characteristics, interface switching ReRAM (Pr0.7Ca0.3MnO3, TiOx) with analog memory characteristics, and HfZrOx ferroelectric device. By optimizing forming and potentiation/depression conditions, we could improve conductance linearity and MLC characteristics of filamentary synapse device. Interface ReRAM has better MLC characteristics with limited retention and conductance linearity. By controlling the reactivity of metal electrode and oxygen concentration in oxide, we can modulate the synapse characteristics. Ferroelectric device exhibits good retention characteristics but it requires 3-terminal device.
To overcome the limitation of conventional CMOS neuron, we have investigated NbO2-IMT device for oscillator neuron applications. We have confirmed feasibility of pattern recognition using IMT oscillator device. Based on various synapse device characteristics, we have estimated the pattern recognition accuracy of MNIST handwritten digits and CIFAR-10 dataset. We have confirmed that synapse device characteristics directly affect pattern recognition accuracy.