Advances in Machine-Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level

Machine-learning-enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine-learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high-dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)-enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip-level edge computing, although promising, still faces challenges such as energy-efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward-looking perspective on the future of machine-learning-enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field. The integration of nanosensors and clouds artificial intelligence catalyzes advancements across diverse domains. Meanwhile, edge and neuromorphic computing combine for efficient, real-time data processing, inspired by the human brain. In this review, recent developments in cloud computing and edge computing are introduced for assisting photonic design and augmenting sensing performance by extracting essential features from high-dimensional data.image (c) 2023 WILEY-VCH GmbH

Automated summary

Machine-learning-enhanced nanosensors show great potential in the field of sensor technology due to their adaptive and predictive capabilities. This paper reviews the advancements in cloud computing, edge computing, and neuromorphic computing, and provides a perspective on the future of machine-learning-enhanced nanosensors.