# 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?
\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 MEMS micro-engine data containing & & & & Trained NN could accurately estimate the reliability by mapping the \\ attributes, such as humidity, operating & & & MAE, SD, & attributes to a reference value and \\ frequency, resonant frequency, spring & & & R-squared, MSE, & minimizing the error. As a result, it \\ collected. Data were limited and & & & RMSE & could optimize the process by \\ contained both bi-modal and uni-modal & & & & sensitivity analysis of the \\ distribution. & & & & process parameters. \\ \hline Simulated MEMS resonator image data & & & DL model could effectively create \\ contained several unique resonator & & & & non-linear combinations of the target \\ patterns. & & & RA & structure voxel by voxel faster and \\  & & & & automatically without \\  & & & & any constraints. \\ \hline Raw visible surface defect images were & & & & Transfer-learning was used for \\ collected [100] containing eight defect & & & Accuracy, & efficient parameter estimation for \\ types and did not contain a uniform & & & Precision, Recall, & faster training of the CNN model to \\ definition for defects. Only a few wafers & & & F-measure, AUC & detect wafer defects on a \\ contained multiple defects, whereas a & & & training subset. \\ single defect type was present in others. & & & & training subset. \\ \hline The data contained multiple defect & & & & The advantage of the proposed \\ patterns and noise. The data type used & & & & high-level features using low-level \\ contained both real and synthetic data, & & & Accuracy, SD, & features with mixed patterns, which \\ and the splitter segregated single and & & & ROC, RMSE, & is impossible using shallow ML \\ missed defects. & & & & models. RGRN was used to detect \\  & & & & single defects, and DSCN was used to \\  & & & & detect mixed defect patterns. \\ \hline Circular disk resonator & & & & The Resnet model could learn from \\ two-dimensional images of 100 pixels & & & & the complex physical, structural \\ by 100 pixels were used where the void & & & patterns, which cannot be \\ represented the resonator body and the & & & Run-time & represented explicitly. The model \\  & & & & provided an accurate and much faster \\  & & & & analysis than the traditional FEA. \\ \hline RMSE = Root Mean Square Error, ROC = Receiver Operating Characteristic, SMOTE = Synthetic Minority Oversampling Technique, MAE = Mean Absolute Error, ARE = Average Relative Error, AP = Average Precision, MSE = Mean Squared Error, MAPE = Mean Absolute Percentage Error, WD = Wasserstein Distance, AP = Average Precision, MAP = Mean Average Precision, SD = Standard Deviation, RA = Ranking Accuracy, AUC = Area Under the Curve. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._layers of the proposed model were not optimum for generalized prediction. As can be seen in the following articles [40; 41; 42; 43; 49; 50], computational power limitation is a great challenge faced by many. Sometimes, training of the model was performed with a fixed learning rate and batch size, which can be further optimized and tuned to improve the model performance [43]. Moreover, even if the data were available, the amount or quality of data was not satisfactory, as reported in [40; 41; 42; 43; 48; 49; 55; 56; 62; 64; 78; 88], or sometimes the data collection process was too exhaustive [72; 77; 85]. This is why synthetic data generation [101] is gaining more and more attention for robust model creation. There exist other challenges from an adaptation and implementation perspective as well. The following lists the number of limitations posed by AI-based solution incorporation in the MEMS field. Challenges of AI-Implementation in MEMS manufacturing:  
* Most AI-based algorithms are data-driven. A larger amount of data provides a better-trained and more robust model.