Technical Review: The Rise of Machine Learning for Sensor Design

Abstract

The integration of machine learning techniques into sensor design represents a transformative paradigm shift across the sensing technology landscape. This technical review explores how computational intelligence is revolutionizing traditional sensor development processes that have historically relied on domain expertise, manual prototyping, and empirical testing. Machine learning algorithms now enable the simultaneous optimization of numerous interdependent mechanical and electrical parameters, navigating complex design spaces with unprecedented efficiency. The synergy between ML and sensing hardware has yielded remarkable advancements in sensitivity, selectivity, power efficiency, and reliability across diverse application domains including wearables, infrastructure monitoring, automotive systems, and industrial sensing. Key ML approaches including supervised learning, evolutionary algorithms, reinforcement learning, and digital twin technologies are transforming every phase of sensor development. Despite implementation challenges related to data requirements, model interpretability, validation protocols, and computational infrastructure needs, innovative solutions continue to emerge. Looking forward, the field progresses toward fully automated design systems, integration with additive manufacturing, self-optimizing sensors, edge computing implementation, and biomimetic sensing architectures. These developments collectively indicate a future where machine learning becomes the standard paradigm for sensor design, delivering more capable sensing technologies while dramatically reducing development timelines and costs.