# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 4 Artificial Intelligence Application in MEMS System: Current Trends
### AI Applications in the MEMS Manufacturing and Design Process  
MEMS reliability and fault mechanism research is still in its early stages. Failure mechanisms differ significantly from those found in traditional microelectronics. Faults or defects arise due to surface interaction energy and intermolecular forces. Fault detection and maintenance are necessary for the MEMS fabrication process for reliability. Keeping this in mind, [PERSON] et al. [77] proposed a fusion of a CNN and a Robust Heteroscedastic Probabilistic Neural Network (RHPNN) for fault detection. The aim was to provide a built-in self-test mechanism to implement in the final testing round. Quality Control is a significant step in generating a superior category of sensors, which is why defect detection is ubiquitous for MEMS manufacturing. [PERSON] et al. [78] proposed a CNN-based defect detection scheme using image processing techniques for pressure sensor chip packaging. The proposed accurate-detection CNN (ADCNN) algorithm was used to detect defects, such as chip damage, chip scratch, wrinkles on the glass surface, the broken bond of the gold and aluminum wires, etc., with a mean average precision of 92.39%. [PERSON] et al. [79] used transfer learning to identify, evaluate, and extract necessary parameters for manufacturing defect detection in a short time. To prevent losses caused by tool wear or tool damage, Tool Condition Monitoring (TCM) adopts appropriate sensor signal processing techniques to monitor and predict the cutter state. An effective TCM system may boost output and ensure product quality, significantly impacting machining effectiveness. TCM is, therefore, quite significant in the manufacturing sector. [PERSON] et al. [80], and [PERSON] et al. [81] demonstrated that incorporating ML-based approaches, such as tree-based models or the Bayesian optimization approach, can reduce maintenance time. Using CAD tools, MEMS simulation and modeling are represented by non-linear partial differential equations (PDE). Traditional methods, such as finite-element methods (FEM) or finite-difference methods (FDM), are computationally heavy, creating a bottleneck for many simulations. To tackle this issue, [PERSON] et al. [82] proposed an NN-based method for dynamic simulation and analysis of non-linearity in a MEMS-based system. The proposed dimensionality reduction method consists of a Generalized Hebbian Algorithm (GHA) [83]. GHA is based on the principle component analysis (PCA) of NN and the Galerkin procedure. The authors claimed that this proposed model could replicate the design and simulation method of PDE and can handle a large number of simulations in less time, using less memory space. This can be very helpful to the MEMS system designer for optimizing the design process. In the technical report [84], author [PERSON] provided a comparative analysis for reliability estimation and prediction using NN on the MEMS device design phase using the component attributes. The reliability of MEMS development has been a crucial factor and is worth looking into. The author proposed a framework for the MEMS reliability modeling, a novel addition to the MEMS design. [PERSON] et al. [85] proposed a data-driven deep neural network-based approach to replace the conventional FEA for the MEMS design cycle. The author designed a non-parameterized NN-based model and trained it using geometric patterns to estimate the MEMS structure accurately, indicating the possibility of defect prediction in the microfabrication process. Due to the increasing complexity of the MEMS manufacturing process, the number of surface defects tends to increase. It is crucial to detect these defects and identify the root cause for yield improvement and overall process optimization. [PERSON] et al. [86] used a faster R-CNN, which they retrained multiple times to detect wafer defects with an accuracy of 98%. [PERSON] et al. [87] also used a CNN model to inspect wafer maps visually. [PERSON] et al.