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]

中文翻译：

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用于减少 MEMS 加速度计参数提取测试时间的迁移学习

在 MEMS 传感器的最终测试期间提取参数是一项时间紧迫的挑战。不断发展的小型化、测试激励和结构复杂性导致系统微分方程的非线性耦合和不均匀性，这些方程无法线性化，因此依赖于缓慢的数值求解方法或需要许多标记数据的机器学习算法。提出了一种转移学习方法，利用高复杂性的 ASIC-MEMS 模型来生成模拟设备的 Monte-Carlo，这些模型用于在参数提取任务上对神经网络进行预训练。第一步，表明对于高质量因子和低质量因子系统，神经网络不仅能够拟合在最终测试期间记录的时间序列与两个性能参数自然频率和阻尼因子之间的关系，还能够提取布朗噪声、质量和外延层厚度。随后，表明转移学习方法对于确定参数特别有用，这些参数在最终测试期间无法直接测量，并且在恶劣的生产测试环境中为系统记录标记数据（如布朗噪声）是昂贵的。如果只有很少的标记样本可用——在执行的实验中，25 台被测设备就足够了——迁移学习方法优于纯粹基于测量数据训练的神经网络。这些发现强调了迁移学习方法的实际进展，并推动了对 MEMS 传感器参数提取领域的进一步应用的评估。[2020-0375]