Deep Learning for Optical Tweezers DeepCalib Implementation for Brownian Motion with Delayed Feedback

Abstract

Brownian motion with delayed feedback, theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming approximation method in order to calibrate stiffness and to measure the delay in harmonic trap case.