An Artificial Neural Network Method for High-Accurate and High-Efficient MEMS Pressure Sensor Design

Abstract

Microelectromechanical system (MEMS) devices have numerous advantages including small sizes, high performance, and easy integration capability and thus have been widely used in the Internet of Things (IoT). A typical MEMS device usually includes a set of performance parameters, and each parameter is sensitive to the device geometries but with different regularities and weights, thus resulting in the complexity of MEMS device design. The conventional design method is mainly based on iterative finite-element (FE) simulation and optimization, which is time-consuming and inefficient. To address the above issues, a bidirectional artificial neural network (ANN)-based method is explored and used as the design method by using an MEMS pressure sensor as a design example. First, a forward ANN with the geometries and performance as the input and output, respectively, is trained and constructed, which can accurately predict the performance. Then, an inverse ANN with the performance and geometries as the input and output, respectively, is also investigated. By means of a tandem network, the nonuniqueness issue of the inverse ANN caused by a one-to-many response from the input to the output can be well addressed. This tandem network can output the corresponding geometries instantly according to the target performance. This work demonstrates the great potential of the ANN as a new and facile strategy in MEMS device design.