Neuromorphic computing, as the name suggests mimics the operation of neurons in the decision making process of our brain. The implementation of neuromorphic computing at the hardware level can be realized by oxide-based memristors, spintronic memories etc.In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. multi-level storage capability for an electronic synapse device. One possible way of implementing the learning capabilities and performance of a neuromorphic circuit composed of a RRAM cross-point array of synapses and complementary metal–oxide–semiconductor neuron circuits. These developments can open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.

References:

• Reliability of analog resistive switching memory for neuromorphic computing, Appl. Phys. Rev. 7, 011301 (2020)

• A comprehensive review on emerging artificial neuromorphic devices, Appl. Phys. Rev. 7, 011312 (2020)

• RRAM-based synapse devices for neuromorphic systems, Faraday Discuss., 213, 421 (2019)

• Park, Sangsu, Jinwoo Noh, Myung-lae Choo, Ahmad Muqeem Sheri, Man Chang, Young-Bae Kim, Chang Jung Kim et al. "Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device." nanotechnology 24, no. 38 (2013): 384009.

• Lastras-Montaño, Miguel Angel, and Kwang-Ting Cheng. "Resistive random-access memory based on ratioed memristors." Nature Electronics 1, no. 8 (2018): 466-472.

• Ielmini, Daniele, and H-S. Philip Wong. "In-memory computing with resistive switching devices." Nature Electronics 1, no. 6 (2018): 333-343.