# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 5 Review Summary
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Year** & \begin{tabular}{c} **Algorithm** \\ **Type** \\ \end{tabular} & **Category** & \
\begin{tabular}{c} **Data** \\ **Type** \\ \end{tabular} & **Data Size** & **Problem Type** &
\begin{tabular}{c} **MEMS** \\ **Sensor** \\ \end{tabular} & **Key Findings** & **Ref.** \\ \hline
2019 & NAS-RNN & Time-Series & Real & 15 & \begin{tabular}{c} Noise \\ Modeling \\ \end{tabular} & \begin{tabular}{c} Gyroscope \\ (MEMS IMU \\ STIM300) \\ \end{tabular} & \begin{tabular}{c} The proposed NAS-RNN \\ was used to de-noise and \\ decrease the standard \\ deviation of the three-axis \\ gyroscope compared to \\ the raw signal. NAS-RNN \\ also outperformed \\ LSTM-RNN. \\ \end{tabular} & \
\begin{tabular}{c} The proposed NAS-RNN \\ was used to de-noise and \\ decrease the standard \\ deviation of the three-axis \\ gyroscope compared to \\ the raw signal. NAS-RNN \\ \end{tabular} &
\begin{tabular}{c} [42] \\ \end{tabular} \\ \hline
2018 & SRU-RNN & Time Series & Real & 10,000 & \begin{tabular}{c} Noise \\ Modeling \\ \end{tabular} & Gyroscope & \
\begin{tabular}{c} A single-layer SRU-RNN \\ was used to de-noise the \\ three-axis MEMS \\ gyroscope. \\ \end{tabular} &
\begin{tabular}{c} [43] \\ \end{tabular} \\ \hline
2018 & BP-NN & Regression & Real & - & \begin{tabular}{c} Thermal \\ Calibration \\ \end{tabular} & Gyroscope & \
\begin{tabular}{c} Alternate to the \\ traditional polynomial \\ fitting for temperature \\ compensation, the BPNN \\ increased the \\ performance by 20\%. \\ \end{tabular} &
\begin{tabular}{c} [44] \\ \end{tabular} \\ \hline
2009 & BP-NN & Regression & Real & - & \begin{tabular}{c} Thermal \\ Calibration \\ \end{tabular} & \begin{tabular}{c} Micro- \\ gyroscope \\ \end{tabular} & \begin{tabular}{c} For the temperature \\ compensation, BP-NN \\ was used, and an \\ integral-separated PID \\ (Proportion Integration \\ Differentiation) system \\ was used for the \\ temperature control \\ system design. \\ \end{tabular} & \
\begin{tabular}{c} For the temperature \\ compensation, BP-NN \\ was used, and an \\ integral-separated PID \\ (Proportion Integration \\ Differentiation) system \\ was used for the \\ temperature control \\ system design. \\ \end{tabular} &
\begin{tabular}{c} [45] \\ \end{tabular} \\ \hline
2011 & BP-NN & Regression & Real & \begin{tabular}{c} (\(-35\,^{\circ}\)C)- \\ (+80 \({}^{\circ}\)C) temp. \\ range \\ \end{tabular} & \begin{tabular}{c} Temperature \\ Drift Compensation \\ \end{tabular} & Gyroscope & \
\begin{tabular}{c} The study aimed to \\ understand the effect of \\ temp. on gyroscope \\ signal drift. \\ \end{tabular} &
\begin{tabular}{c} [47] \\ \end{tabular} \\ \hline
2017 & RBF-NN & \begin{tabular}{c} Function approximation \\ \end{tabular} & Real & - & \begin{tabular}{c} Adaptive \\ Control \\ \end{tabular} & Gyroscope & \
\begin{tabular}{c} Fully tuned RBF-NN was \\ proposed to remove \\ model uncertainty and \\ external disturbances. \\ \end{tabular} &
\begin{tabular}{c} [48] \\ \end{tabular} \\ \hline
2017 & \begin{tabular}{c} PSR, C-C, \\ CPSO-LSSVM \\ \end{tabular} & Regression & Real & 15,000 & \begin{tabular}{c} Temperature \\ Drift Compensation \\ \end{tabular} & Gyroscope & \begin{tabular}{c} MEMS gyroscope \\ drift reconstruction was \\ performed using PSR and \\ C-C method, which helps \\ with dimensionality \\ reduction. CSPO-LSSVM \\ was used to model the \\ MEMS gyroscope random \\ drift that outperformed \\ BP-ANN. \\ \end{tabular} & \begin{tabular}{c} MEMS gyroscope \\ drift \\ compensation of the \\ gyroscope was performed \\ using SVM that is \\ optimized using GA. This \\ is a simple, expusisite, and \\ powerful solution. \\ \end{tabular} & \begin{tabular}{c} Temperature \\ gyroscope \\ \end{tabular} & \
\begin{tabular}{c} The temperature drift \\ gyroscope was performed \\ using SVM that is \\ optimized using GA. This \\ is a simple, expusisite, and \\ powerful solution. \\ \end{tabular} &
\begin{tabular}{c} [50] \\ \end{tabular} \\ \hline
2020 & GA, BPNN & Regression & Real & 6360 & \begin{tabular}{c} Temperature \\ Drift Compensation \\ \end{tabular} & Accelerometer & \
\begin{tabular}{c} BPNN, optimized using \\ GA, performed 173 times \\ better than traditional \\ polynomial fitting for \\ MEMS resonant \\ accelerometer. \\ \end{tabular} &
\begin{tabular}{c} [51] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}