Affiliation:
1. Faculty of Informatics, Institute of Academia-Industry Innovation, Eötvös Loránd University, Egyetem tér 1-3, 1053 Budapest, Hungary
2. Department of Artificial Intelligence, Eötvös Loránd University, Egyetem tér 1-3, 1053 Budapest, Hungary
Abstract
Micro-electromechanical systems (MEMS) technology-based sensors have found diverse fields of application due to the advancement in semiconductor manufacturing technology, which produces sensitive, low-cost, and powerful sensors. Due to the fabrication of different electrical and mechanical components on a single chip and complex process steps, MEMS sensors are prone to deterministic and random errors. Thus, testing, calibration, and quality control have become obligatory to maintain the quality and reliability of the sensors. This is where Artificial Intelligence (AI) can provide significant benefits, such as handling complex data, performing root cause analysis, efficient feature estimation, process optimization, product improvement, time-saving, automation, fault diagnosis and detection, drift compensation, signal de-noising, etc. Despite several benefits, the embodiment of AI poses multiple challenges. This review paper provides a systematic, in-depth analysis of AI applications in the MEMS-based sensors field for both the product and the system level adaptability by analyzing more than 100 articles. This paper summarizes the state-of-the-art, current trends of AI applications in MEMS sensors and outlines the challenges of AI incorporation in an industrial setting to improve manufacturing processes. Finally, we reflect upon all the findings based on the three proposed research questions to discover the future research scope.
Funder
EIT Digital Knowledge Innovation Community in Hungary
Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund
Subject
General Medicine,General Chemistry
Cited by
3 articles.
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