# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 2 MEMS Background
### MEMS Manufacturing Process  
The MEMS manufacturing process is an extensive and time-consuming endeavor that requires numerous quality checks before completing the entire process. The design requires simultaneously considering devices from electrical, mechanical, and electronic domains. Not only that, the design should consider the cross-domain effect and analyze it. MEMS fabrication is based on chemical etching and photo-lithography. It consists of bulk micro-machining, surface micro-machining, and high-aspect-ratio micro-machining using techniques, such as LIGA (Lithographie, Galvanoformung, Abforming-Lithography, Electroplating, and Molding) [23]. New micro-manufacturing methods, including micro-mechanical cutting, micro-electrical discharge machining, micro-electrochemical machining, micro-forming, laser technology, laser-assisted forming, replication techniques, deposition methods, etc., have been emerging to create a hybrid process. Multiple companies exist, such as MEMSCAP Inc., IntelliSense Software Corp, ABAQUS, Inc., Coventor Inc., etc., and research bodies, such as U.C. Berkeley, Carnegie Mellon University, TU Chemnitz, etc., who are continuously working on providing improved solutions for MEMS design [24]. Figure 5 shows a high-level overview of the MEMS design structure flow [25,26]. Given the numerous steps involved, several challenges occur at different levels, such as design issues, fabrication issues, bulk micro-machining issues, dicing issues, packaging issues, technical issues [27,28] etc.  
As stated in article [29], manufacturing defects can be categorized into the following three categories.  
* Type A defects: These types of defects are evenly random with a stable mean density. There is no repeated occurrence or visible systematic pattern, i.e., the probability of a dice being good or bad is equal. Thus, the root-cause analysis of such defects is not straightforward. Only an accurate and stable process can help reduce these kinds of defects.  
* Type B defects: These types of defects are repeatable and of systematic pattern from wafer to wafer. These defects' source can be generated from anomalies in the process or machine, such as mask induced or during the variation while applying films.
* Type C defects: The most common type of defect seen in semiconductor manufacturing is Type C defect, a combination of Type A and Type B defects. It is essential to eliminate random defects to recognize and eradicate systemic flaws. There can be multiple causes, such as structural defects coming from the raw material, asymmetrical presence of contaminants, irregular presence of defect generation particles, etc.  
All these defects can be visible or invisible. The visible wafer defects can be classified as local, random, center, and scrape based on the visual pattern type. Currently, manual inspections are performed to detect visible surface anomalies, which are prone to erroneous outcomes.  
The lack of a standardized framework to resolve the aforementioned issues obstructs the manufacturing process. However, this can not be achieved easily as the expected solutions are based on the nature and complexity of the problems. The solution requires in-depth domain knowledge and process knowledge and can be cumbersome. Given the rise in the market demand for cheaper, smaller, and high-performance MEMS sensors, organizations and researchers are constantly looking for new means to improve the MEMS sensor production process to meet the market demand. This is where AI can provide a massive benefit for process optimization. In the following section, we will look into several problem areas of MEMS-based manufacturing where AI-based solutions are beneficial to incorporate.