Transfer Learning for Test Time Reduction of Parameter Extraction in MEMS Accelerometers

Parameter extraction during the final test of MEMS sensors poses a highly time-critical challenge. The progressing miniaturization, test stimuli and structural complexity lead to nonlinear couplings and inhomogeneity in system differential equations, which cannot be linearized and are therefore dependent on either slow numerical solution methods or machine learning algorithms requiring many labeled data. A transfer learning approach is presented making use of high complexity ASIC-MEMS models for Monte-Carlo generation of simulated devices, which are used to pre-train neural networks on the task of parameter extraction. In a first step, it is shown that for both, high quality factor and low quality factor systems, neural networks are not only able to fit the relation between time-series recorded during final testing and the two performance parameters natural frequency and damping factor but also to extract Brownian noise, mass, and epitaxial layer thickness. Subsequently, it is shown that the transfer learning approach is particularly useful for the determination of parameters, which cannot be measured directly during the final test and for which it is expensive to record labeled data like Brownian noise for systems in a harsh production test environment. If only very few labeled samples are available – in the performed experiments, 25 devices under test were sufficient - the transfer learning approach outperforms a neural network purely trained on measured data. These findings emphasize the practical advances of the transfer learning approach and motivate the evaluation of further applications in the field of parameter extraction in MEMS sensors. [2020-0375]