A Rapid Design and Fabrication Method for a Capacitive Accelerometer Based on Machine Learning and 3D Printing Techniques

Abstract

MEMS (Micro Electromechanical System) sensors have been increasingly used to detect human movements in health monitoring applications. Usually, a full cycle of design and fabrication of a MEMS sensor such as an accelerometer requires highly professional understanding of device functions and expertise in microfabrication process. However, the advent of internet of things (IoT) brings a large demand for low-cost and highly customizable sensors, which requires fast fabrication and flexible design, even by the customers with limited background knowledge in the device itself. In this work, we present the development of a rapid design and fabrication workflow for accelerometers by combining an artificial neural network (ANN) based inverse design method and a one-step 3D printing fabrication technique. The one-step 3D printing fabrication approach is based on a conductive composite material, a polylactic acid (PLA) polymer with carbon black. In device design, trained bidirectional ANNs were designed to predict the device performance from given design parameters and retrieve the design parameters from the customer requirements of the device performance. A capacitive accelerometer was then designed based on the retrieved geometric parameters and fabricated by an integrated 3D printing process without using any additional metallization and assembly processes. With a sensitivity of 75.2 mV/g and a good dynamic response, the 3D printed accelerometer was shown to be capable of detection and monitoring of human movements. The proposed rapid design and fabrication workflow provides an effective solution to customized and low-cost MEMS devices suitable for IoT applications.