Real-time reconfigurable counter-propagating beam-traps

We present a versatile technique that enhances the axial stability and range in counter-propagating (CP) beam-geometry optical traps. It is based on computer vision to track objects in unison with software implementation of feedback to stabilize particles. In this paper, we experimentally demonstrate the application of this technique by real-time rapid repositioning coupled with a strongly enhanced axial trapping for a plurality of particles of varying sizes.

Also exhibited is an interesting feature of this approach in its ability to automatically adapt and trap objects of varying dimensions which simulates biosamples. By working on differences rather than absolute values, this feedback based technique makes CPtrapping nullify many of the commonly encountered pertubations such as fluctuations in the laser power, vibrations due to mechanical instabilities and other distortions emphasizing its experimental versatility.