Abstract

In the twenty-first century, nano-electronic gadgets have become more and more important for the growth of science and technology. These devices have a wide range of possible uses thanks to their integrated machine learning capabilities, including autonomous robotics, chip-level circuits with improved data processing, and next-generation sensors. The goal of this essay is to examine the difficulties involved in creating and implementing nano-electronic devices with integrated machine learning capabilities. Two independent research trajectories—materials sciences and machine learning—are involved in the creation and application of nanoelectronic devices with integrated machine learning capabilities. Nanoscale electronic devices must, from a materials science perspective, have characteristics that are matched to the particular use. For instance, nanomaterials must have improved conductivity and stability to support machine learning applications. Furthermore, a functional architecture that enables system-level calibration, adjustment, and reconfiguration must include nanoscale electrical devices. Large data sets are required for practical training and model creation in the field of machine learning. New methods are required to process the data non-linearly but effectively in order to exploit these training data sets. These methods would make it possible to extract valuable characteristics from massive data sets and learn complex patterns.