# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 1 Introduction  
Micro-electromechanical systems (MEMS)-based technologies have been in the market for decades due to the rapid integration of MEMS sensors. It has revolutionized market segments, such as consumer electronics, automotive, healthcare, industry 4.0, internet-of-things, etc. Figure 1 shows that the demand for MEMS sensors is ever-increasing and will keep rising, as shown. The growth is due to the highly scalable and efficient manufacturing technology available to mass-produce the sensors at a low cost, mostly in the consumer electronics and automotive industry. The most common and heavily used MEMS inertial sensors are gyroscopes and accelerometers used in consumer devices and automotive [1, 2]. The advancement in the silicon MEMS/CMOS technology at both chip and device levels has enabled further improvement of MEMS sensors in terms of miniaturization, high accuracy, high quality and performance at low cost. MEMS design and manufacturing processes are becoming more complex and diverse due to incorporating newer technologies and their multi-domain structure. Due to this, sensors are prone to various kinds of deterministic and random errors, such as misalignment, white noise, random walk, quantization noise, etc. If these errors are not handled promptly, they can accumulate and affect the behavior of the sensors negatively [3, 4, 5, 6, 7]. This makes the quality checks even more challenging, resulting in increased testing costs. Testing cost consists of wafer-level testing and packaging-related testing costs, contributing to the overall device manufacturing cost [8, 9, 10]. Thus, there is anongoing demand to reduce these costs and improve the production process in hardware and software domains.  
Artificial Intelligence (AI) has been around for decades, and the recent applications of AI are more intensive than ever due to the latest boom in data availability, increased system computational power, and storage capacity. AI applications branch out to multiple sub-disciplines, such as deep learning (DL), machine learning (ML), natural language processing (NLP), computer vision, robotics, etc.  
Figure 2 depicts a high-level overview of different components, types, and sub-fields of AI. Machine learning, which emerged from the statistical background, has shown its strength and convenience from complex data understanding to multidimensional data handling. AI applications are countless, such as autonomous vehicles, predictive maintenance, supply chain optimization, resource optimization, manufacturing process optimization, banking, financing, surveillance, recommendation system, healthcare, marketing, quality inspection, education, etc. [12, 13, 14, 15]. Looking at the current trends, it can be seen that different AI algorithms, such as tree-based algorithms, deep neural networks, and reinforcement learning, are mainly used for industrial domain applications.  
Hence, it is worth looking into the possible amalgamations of these two strong domains, MEMS and AI, to benefit both. This paper surveys AI applications in MEMS-based sensors and related processes. We try to find research opportunities in the MEMS sensors manufacturing domain that are not well explored or less investigated. The rest of the paper is structured as follows. A brief overview of the MEMS sensors types, applications, and MEMS manufacturing process is presented in Section 2. In Section 3, the research methodology and materials used for this review paper are discussed. Section 4 provides a detailed analysis of the current trends of AI applications in the MEMS-based sensors, design, and manufacturing process. A review summary of the papers is presented in a tabular format in Section 5. Section 6 reflects on the three research questions raised in Section 3. It provides an overview of AI implementation benefits and challenges in industrial settings. Finally, we conclude the paper in Section 7.  
Figure 1: MEMS market revenues by application from 2014 to 2024 (projected) worldwide in million USD [11].