FEM-Inclusive Transfer Learning for Bistable Piezoelectric MEMS Energy Harvester Design

In this article, a bistable piezoelectric MEMS energy harvester is presented to operate in a low-frequency range, around 100-200 Hz. The proposed design has an M-shaped structure with a couple of proof masses to not only lower the operating frequencies but also enlarge the frequency bandwidth. This specific structure has multide-grees of freedom, making it fit for a bistable piezoelectric energy harvester on the MEMS scale. An artificial neural network (ANN) is used to tackle this design in order to facili-tate the optimization process and determine proper physical dimensions. To improve the accuracy and boost the training process of deep neural network (DNN), we utilize a transfer learning technique in this work. The analytical modeling and finite-element modeling (FEM) simulation data have been used for the DNN model training. Here, a DNN is first trained with a large dataset computed from the lumped-parameter model, and then, the trained network is transferred to a new DNN model for another round of training with a small dataset of highly accurate FEM simulation data samples to further reduce the estimation error. It is shown that the new model can estimate the device features with over 94% accuracy, which is considerably higher than the regular DNN. Next, the trained model is used as a performance estimator in a genetic algorithm (GA) to optimize the topology of the device to improve the operating frequency range and the generated voltage. An optimized design with a total volume of 1.02 mm(3) was fabricated by the micromachining process. Our experimental results confirm that the proposed transfer-learning-based method can not only reduce the prototype's first and second resonant frequencies to 123.8 and 175.7 Hz, respectively, but also enhance the generated power up to 2.83 mu W under 0.2-g input acceleration.