Marco Polin [1], Kosta Ladavac [1,2], Sang-Hyuk Lee [1],
Yael Roichman [1] and David G. Grier [1]
[1] Dept. of Physics and Center for Soft Matter Research,
New York University, New York, NY 10003
[2] Department of Physics, James Franck Institute and
Institute for Biophysical Dynamics, The University of Chicago,
Chicago, IL 60637
Date: July 18, 2005
A single laser beam brought to a focus with a strongly converging lens forms a type of optical trap widely known as an optical tweezer (1). Multiple beams of light passing simultaneously through the lens' input pupil focus to multiple optical tweezers, each at a location determined by the associated beam's angle of incidence and degree of collimation as it enters the lens. Their intersection at the input pupil yields an interference pattern whose amplitude and phase corrugations characterize the downstream trapping pattern. Imposing the same modulations on a single incident beam at the input pupil would yield the same pattern of traps. Such wavefront modification can be performed by a computer-designed diffractive optical element (DOE), or hologram.
Holographic optical trapping (HOT) uses computer-generated holograms (CGHs) to project arbitrary configurations of optical traps (2,3,4,5,6), and so provides exceptional control over microscopic materials dispersed in fluid media. Holographic micromanipulation provides the basis for a rapidly growing field of applications in the physical and biological sciences as well as in industry (7).
This article describes refinements to the HOT technique that help to optimize the traps' performance. It also introduces self-consistent and statistically optimal methods for characterizing their performance. Section 1 describes modifications to the basic HOT optical train that compensate for practical limitations of dynamic holography. Section 2 discusses a direct search algorithm for HOT CGH computation that is both faster and more accurate than commonly used iterative refinement algorithms. Together, these modifications yield marked improvements in the holographic traps' performance that can be quantified rapidly using techniques introduced in Section 3. These techniques are based on optimal statistical analysis of trapped colloidal spheres' thermally-driven motions, and lend themselves to simultaneous real-time characterization and optimization of entire arrays of traps through digital video microscopy. Such adaptive optimization is demonstrated experimentally in Section 4.
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