Bridging the Gap From Vague Design Requirements to Feasible Structure: Deep Learning Model for Parameterized MEMS Sensor Design

Abstract

The design of MEMS sensor presents a significant challenge in identifying feasible structures that align with specific performance criteria. Traditionally, this process demands extensive design expertise and iterative simulations, leading to time-intensive workflows. While recent advancements have introduced deep learning (DL) models to expedite this process, they are limited to handling simple scenarios with precise performance values and fixed dimensions as inputs, often overlooking the uncertainty inherent in real design scenarios, such as vague range requirements and variable input dimensions. To address this issue, this study introduces a novel DL-based design model along with corresponding modeling strategies. The proposed model consists of a search network (SN), a validation network (VN), and a precision optimizer (PO). Initially, design requirements of various types and dimensions are transformed into a standardized input vector to address diverse design scenarios, which is then processed by the SN to generate a feasible structure. The VN, trained prior to the SN, validates the structure and generates training data for the SN. In cases where the model output fails to sufficiently align with the requirements, the PO is deployed to minimize the design error. Validation of the proposed model was conducted using a piezoresistive acceleration sensor across 100000 distinct design requirements. The results demonstrate an overall design accuracy (DA) of 92.64% on the testing data. Following 1000 iterations leveraging the proposed PO, the DA improves to 93.84%. Notably, each design iteration and optimization using the PO only requires approximately 0.1 ms, significantly boosting the design efficiency of MEMS sensors.