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.

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Micro-electromechanical systems (MEMS) manufacturing is a highly complex process consisting of several hundred steps. The real-time data captured during those process control steps results in a huge data base. Analysis of that enormous amount of data in real-time with high sample rate during production for eventual fault detection and prediction is very challenging. The parameters are highly nonlinear and complex in nature. This makes it difficult for the traditional methods to find this hidden pattern. Advances in Machine Learning (ML) paves the path to investigate the vast dataset and find the hidden complex pattern for early failure prediction and root cause analysis. In the paper, we focus on exploring the applicability of ML methods for the prediction of the affected MEMS inertial sensors using different ML methods. We use statistical analysis to investigate the results to learn about the root cause effect. Finally, we investigate the optimal set of sub-parameters needed for the chosen methods to achieve maximum performance without over-fitting and redundancy.