A Novel Optimization and Yield Analysis Method for MEMS Devices Combining Bi-Fidelity Deep Neural Network and Active Subspace

The development of high-performance MEMS devices necessitates efficient design optimization and yield analysis in the presence of unavoidable process variations. The existing algorithms are not able to integrate the different requirements of optimization and uncertainty quantification (UQ), highlighting the necessity for a comprehensive algorithmic framework. To this end, a novel algorithm framework is proposed for the optimization and UQ of MEMS devices. The deep neural network (DNN) and polynomial chaos expansion (PCE) are respectively leveraged to build the global surrogate model for the nondominated sorting genetic algorithm II (NSGA-II) and the local surrogate model for the UQ analysis. The DNN is refined by the bi-fidelity data training scheme to reduce the training data demanded, and the active subspace dimension reduction (AS) is employed to fully utilize the information within the global surrogate model, thereby minimizing the computational resources needed for the local surrogate model construction. This algorithm is applied to a kind of MEMS thermal flow sensor. The proposed algorithmic simultaneously increases sensor sensitivity by 14.77% while reducing computational expenditure by 44.4% during the optimization phase. Besides, the yield analysis is also derived accurately, with computational cost reductions of 73.0%. These results indicate that this novel algorithm supplies an effective, accurate, and efficient technique for the optimization and yield analysis for MEMS devices. [2025-0231]