Graph neural networks for parameter estimation in micro-electro-mechanical system testing

Abstract

Micro-electro-mechanical systems (MEMS) are of great importance in a broad range of applications including vehicle safety and consumer electronics. During the testing of these devices, large heterogeneous data sets containing a variety of parameters are recorded. Aiming to substitute costly measurements as well as to gain insight into the relations among the measured parameters, graph neural networks (GNNs) are investigated. Thus, the questions are addressed whether for inference of MEMS final module level test parameters, working on graph structures leads to an improvement of the predictive performance compared to the analysis via standard machine learning approaches on tabular data and how the graph structure and learning algorithm contribute to the overall performance. To evaluate this, in an empirical study different graph representations of the acquired test data were set up. On these, four different state-of-the-art GNN architectures were trained and compared on the task of raw sensitivity prediction for a MEMS gyroscope. Whereas the GNNs performed on par with a light gradient boosting machine, neural network and multivariate adaptive regression splines model used as baseline on the complete data set, in the presence of sparse data, the GNNs outperformed the baseline methods in terms of the overall root-mean-square error (RMSE) and achieved distinct improvement in the maximum error when trained on data with similar sparsity rates as observed during the validation. • Proposal and demonstration of GNNs for parameter estimation in MEMS testing. • Application to sensitivity estimation at final module testing of MEMS gyroscopes. • Evaluation of different graph construction variants and four GNN architectures. • Distinct performance improvement in the case of sparse measurement data.