Neuromorphic devices and systems have attracted great attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide-semiconductor-based hardware. The emerging paradigm of neuromorphic computing is inspired by neural networks of the brain and based on energy-efficient hardware for information processing. To create devices that mimic what occurs in our brains’ neurons and synapses, the scientific community must overcome a fundamental molecular engineering challenge: how to design devices that exhibit controllable and energy-efficient transition between different resistive states triggered by incoming stimuli.
However, these approaches have drawbacks in power consumption and learning speed. An energy efficient neuromorphic computing system/device requires hardware that can mimic the functions of brain. Also, the conventional von Neumann computing requires a large amount of data transmission between central processing units (CPUs) and main memory unit. These problems may become serious when processing complex information. Therefore, various nanomaterials have been introduced for the development of neuromorphic devices. To overcome this issues, we have studied realization and properties of promising TMOs and 2D materials for the application of neuromorphic computing devices that can emulate the functions of neurons and spike timing-dependent plasticity that is used for unsupervised learning and implement the function of synapses.