Technology field / OU or Centre
Laser Diagnostics/CSIR National Laser Centre and Friedrich Schiller
Universität of Jena
The decomposition of a laser beam into a superposition of modes has been known for a long time and has found various applications, most notably in pattern recognition and related fields. This is referred to as modal decomposition. There are clear advantages in executing such modal decomposition of multimode laser beams, and several attempts have been made with varying degrees of success.
Presently we have the tools to perform an optimal modal decomposition using rewritable digital holograms, thus enabling full characterisation of an unknown laser beam in real time. The modal decomposition of a laser beam involves extracting the phases and amplitudes associated with the laser beam’s constituent components. Once the phases and amplitudes are known then all the physical quantities of the laser beam can be determined, e.g.: its intensity, phase, wavefront, beam quality factor, Poynting vector and orbital angular momentum density.
Direct-users that will be targeted are the laser and laser related
equipment manufacturers that require analysis of laser output beams. This may also prove of benefit to commercial fiber product providers further down the line.
The technology will allow easy and efficient measurement of several laser parameters simultaneously. This will make systems that need to incorporate this type of technology much more flexible and they can dispense with complicated external optical systems, thereby allowing them to access new applications or re-address existing applications that have been inaccessible due to cost or dimensional restrictions.
Regarding the communications industry, the technology being proposed will present the direct-users (communication service providers) with several benefits with some alterations to their existing systems. Regarding the end-users, due to these improvements, they can purchase much more bandwidth at a much lower cost thereby giving them faster access to data.
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