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 multidegrees 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 facilitate 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 mm3 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
$2.83 ~\mu \text{W}$
under 0.2-g input acceleration.