In an acousto-optic modulator,the electrode shape plays an important role in performance,since it affects the distribution of the acoustic field.The acousto-optic modulator based on the conventional rectangular electr...In an acousto-optic modulator,the electrode shape plays an important role in performance,since it affects the distribution of the acoustic field.The acousto-optic modulator based on the conventional rectangular electrode has the problems of low energy efficiency and small modulation bandwidth due to an imperfect acoustic field.In this paper,a new serrated periodic electrode has been proposed for using acousto-optic modulator transducers.The proposed electrode has the following advantages.By using serrated periodic electrodes to suppress the sidelobes,the collimation of the acoustic field in the direction perpendicular to the light incidence is improved.This makes the acousto-optic modulator have a stable diffraction efficiency fluctuation and high energy efficiency.In addition,the electrode has a large divergence angle in the direction of light incidence,so a large bandwidth can be obtained.The simulations and experiments demonstrate that the serrated periodic electrode has an increased bandwidth and high energy efficiency.展开更多
The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it...The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it would be very time-and resource-consuming to study all these cases using numerical simulation.In this paper,we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features.Design candidates are represented in a nonparameterized,topologically unconstrained form using pixelated black-and-white images.After sufficient training,a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis.As an example,we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators.With reasonable training,our deep learning neural network becomes a high-speed,high-accuracy calculator:it can identify the flexural mode frequency and the quality factor 4.6×10^(3)times and 2.6×10^(4)times faster,respectively,than conventional numerical simulation packages,with good accuracies of 98.8±1.6%and 96.8±3.1%,respectively.When simultaneously predicting the frequency and the quality factor,up to~96.0%of the total computation time can be saved during the design process.The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.展开更多
基金supported by the National Key Research and Development Program of China(Nos.2021YFB3602500 and 2021YFB3602502)。
文摘In an acousto-optic modulator,the electrode shape plays an important role in performance,since it affects the distribution of the acoustic field.The acousto-optic modulator based on the conventional rectangular electrode has the problems of low energy efficiency and small modulation bandwidth due to an imperfect acoustic field.In this paper,a new serrated periodic electrode has been proposed for using acousto-optic modulator transducers.The proposed electrode has the following advantages.By using serrated periodic electrodes to suppress the sidelobes,the collimation of the acoustic field in the direction perpendicular to the light incidence is improved.This makes the acousto-optic modulator have a stable diffraction efficiency fluctuation and high energy efficiency.In addition,the electrode has a large divergence angle in the direction of light incidence,so a large bandwidth can be obtained.The simulations and experiments demonstrate that the serrated periodic electrode has an increased bandwidth and high energy efficiency.
文摘The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it would be very time-and resource-consuming to study all these cases using numerical simulation.In this paper,we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features.Design candidates are represented in a nonparameterized,topologically unconstrained form using pixelated black-and-white images.After sufficient training,a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis.As an example,we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators.With reasonable training,our deep learning neural network becomes a high-speed,high-accuracy calculator:it can identify the flexural mode frequency and the quality factor 4.6×10^(3)times and 2.6×10^(4)times faster,respectively,than conventional numerical simulation packages,with good accuracies of 98.8±1.6%and 96.8±3.1%,respectively.When simultaneously predicting the frequency and the quality factor,up to~96.0%of the total computation time can be saved during the design process.The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.
