A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and
synapses, enable computing machines and systems to execute cognitive tasks, thus …

Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …

Memristor devices have diverse physical models depending on their structure. In addition,
the physical properties of memristors are described using complex differential equations …

The modern IC supply chain encompasses a large number of steps and manufacturers. In
many applications it is critically important that chips are of the right quality and are assured …

Mass characterisation of emerging memory devices is an essential step in modelling their
behaviour for integration within a standard design flow for existing integrated circuit …

The memristor is regarded as one of the promising possibilities for next-generation
computing systems due to its small size, easy construction, and low power consumption …

In this paper, we present a framework of modeling memristor noise for circuit simulators
using physics-informed neural networks (PINNs). The variability of the memristor that is …

This paper proposes a memristor-assisted sign-based background calibration scheme for
analog-to-digital converters (ADCs). The scheme was implemented and validated in a 12-bit …

The capability of in-memory computation, reconfigurability, low power operation as well as
multistate operation of the memristive device deems them a suitable candidate for designing …