— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device model based on the measured characteristics of the CBRAM device. The applicability of the neuron is demonstrated by constructing a neural network system and applying it to pattern reconstructions that can recall the original patterns from noisy patterns. With these CBRAM-based neurons, a reduction in the area and power of neuromorphic chips is expected in comparison with CMOS-only neuron implementations.
Tilo Schwalger, Valentin Marc Schmutz
Mohammad Khaja Nazeeruddin, Paul Joseph Dyson, Yong Ding, Li Tao, Yao Zhang, Jun Zhang, Bowen Jiang
Martin Louis Lucien Rémy Barry