# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 6 Discussion
### Q2: What Are the Advantages of AI-Based Solutions Compared to State-of-the-Art Solutions? With the advancement of graphics processor units (GPU), data abundance, and less processing time [92, 93], AI-based solutions have been integrated with more research areas. It can be seen in Figure 9 that since 2017, there has been a growth in the number of published articles; without any doubt, the most used algorithm type is ANN. Among all the papers used for the review, 78% of the authors used ANN-based solutions. ANN is useful for estimating complex and unknown functions with incomplete a priori knowledge. It has high fault tolerance, auto-correction mechanism, and parallel processing with easy application [94]. Looking deeper into the subcategories of the ANN algorithms, variants of CNN type were used the most (31% of the total articles). Besides neural network-based models, tree-based models such as RF, AdaBoost, DT, and XGBoost and statistical models such as SVM were also used. Tree-based models are relatively less complex and straightforward and easily explainable. Statistical models, such as SVM, NB, and PCA, are physically realizable and easily visualized. The following part summarizes the benefits obtained by using AI-based solutions. Advantages of Using AI:  
* Hidden patterns, such as cross-correlation, in the data that are impossible with traditional methods can be discovered and visualized. * Feature estimation can be enhanced in combination with traditional methods. * Automated monitoring systems can be built for FDD and predictive maintenance without any human in the loop. This reduces manual error and improves process accuracy. * Elimination of prior knowledge dependency. * MEMS manufacturing process improvement by eliminating iterative calibration steps with AI predictions. * In the case of MEMS manufacturing, the sensing element fabrication process is subjected to different kinds of failures. AI methods can accurately perform image classification. it has proven to be useful for fault detection and recognizing substrate defects. * Transfer learning helps apply previously gained knowledge to identify and detect features related to defects before the final test. * AI-based models can replace conventional design tools such as FEA for predicting MEMS structural design steps with higher accuracy in less time. * Overall, time-saving and process optimization can be achieved by incorporating AI. Table 4 provides an in-depth analysis of the data features, data availability, model performance metrics used, and proposed AI algorithm advantages compared to the state-of-the-art solution used in the articles analyzed in Table 3. The _Data Features and Attributes_ column discusses the type, complexity, and limitation of the data used. Whether the data is available publicly is mentioned in the _Data Availability_ column. The type of assessment criterion used to validate the model, or the proposed solution is mentioned in the _Performance Metrics_ column. In the last column, _Implemented AI Algorithm Advantage_, key benefits of the proposed algorithm presented in the articles are highlighted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Data Features and Attributes** & **Data Availability** & **Performance Metrics** & **Implemented AI Algorithm Advantage** \\ \hline Raw data were obtained from An Akasa & & & ANN-based MLP algorithms could efficiently model complex systems consisting of non-linear data. \\ AK-FN059 12 cm Viper cooling fan and an MMA8452Q accelerometer. Different vibration measurements were obtained with 3 weight distributions and 17 rotation speeds at a frequency of 20 ms for 1 min. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed analysis of the data and model performance. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Ref. ** & **Data Features and Attributes** & **Data Availability** & **Performance Metrics** & **Implemented AI Algorithm** \\ \hline \multirow{8}{*}{[34]} & Data used were highly imbalanced.