# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 4 Artificial Intelligence Application in MEMS System: Current Trends
### AI Application in MEMS-Based Sensors  
A MEMS IMU unit consists of a multi-axis gyroscope and accelerometer, which are used for positioning and navigation systems when integrated with the global positioning system (GPS). A MEMS IMU system is preferred over traditional solutions due to its higher accuracy, small size, and lower cost. Compared to fiber or laser-based IMUs, MEMS-based IMUs are susceptible to more deterministic and random errors, such as measurement, quantization noise, alignment, bias, etc. Such uncompensated errors accumulate over time and adversely impact the precision and sensitivity of the sensors. There exist mathematical and statistical model-based calibration methods for error compensation. However, random errors (which contain high-frequency and low-frequency components) introduce drift and bias into the Inertial Navigation System (INS). Thus, adequate signal de-noising schemes are required to remove random errors. Allan Variance, Auto-Regressive, Moving Average, and Wavelet De-noising methods exist for error modeling, but this still requires an accurate model to ensure accuracy. The following papers [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] show that DL-based error compensation models can learn more characteristics of the reference signals, such as identifying the particular accelerations and angular velocities, in comparison to traditional calibration processes, such as the six-position static test and rate test. It is crucial to note that the same deep learning networks may eliminate different error sources from IMUs of any performance grade. [PERSON] et al. [40] proposed a Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) for MEMS IMU (MSI3200) de-noising, which outperformed the Auto-Regressive and Moving Average (ARMA) method. This paper was limited to a static dataset and limited layers of LSTM-RNN and thus needed more experimentation. [PERSON] et al. [41] proposed a heterogeneous deep learning recurrent neural network (RNN) design to suppress the MEMS gyroscope signal noise. Here, two deep-layered RNN algorithms, Gated Recurring Unit (GRU) and Long Short Term Memory (LSTM), were used individually and mixed for the experimentation. The outcome of this analysis showed promising results for significant noise reduction of the MEMS gyroscope using the LSTM-GRU network. Figure 8: AI implementation process [PERSON] et al. [42] described a method using the three-axis Neural Architecture Search Recurrent Neural Network (NAS-RNN) for noise modeling and compensation of MEMS Gyroscope for MEMS IMU STIM300 unit. The limitation of this approach was that the experiments were performed only on the STIM300 unit and using only RNN models. Nevertheless, this research paper demonstrates that incorporating DL in MEMS gyroscope can improve accuracy significantly by reducing noise. Nevertheless, this research paper showcases the strength of incorporating a deep learning module in the MEMS gyroscope that can significantly improve the accuracy by reducing the noise. [PERSON] et al. [43] proposed an RNN variant called Simple Recurrent Unit (SRU-RNN) for de-nosing MEMS IMU MSI3200 gyroscope signals. The authors claimed that SRU-RNN provided significant advantages over the conventional approaches using SVM, LSTM, or LSTM-RNN methods. This research was limited to a single-layer SRU-RNN and fixed parameters. Thermal calibration of the MEMS gyroscope is required to compensate for error drift, which can affect the accuracy of the gyroscopes. Traditionally, polynomial fitting is performed to overcome this. The authors of [44, 45, 46, 47] have shown that Artificial Neural Networks (ANN) have superior performance compared to the traditional method. [PERSON] et al. [48] proposed a radial basis function (RBF) neural network-based control scheme to reduce non-linearity during fabrication, such as drift, to improve the robustness of the MEMS gyroscope. However, the proposed method might be prone to over-fitting, which is why [PERSON] et al.