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Reduced-Search Calibration for Tunable Lasers

IP.com Disclosure Number: IPCOM000010808D
Publication Date: 2003-Jan-22
Document File: 4 page(s) / 135K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a reduced-search method that uses feedback control for the frequency/wavelength channel calibration of tunable lasers with non-ideal tuning maps. Benefits include a reduction in calibration time and cost.

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Reduced-Search Calibration for Tunable Lasers

Disclosed is a reduced-search method that uses feedback control for the frequency/wavelength channel calibration of tunable lasers with non-ideal tuning maps. Benefits include a reduction in calibration time and cost.

Background

Tunable lasers have a tuning map that covers a multi-dimensional space of order n, where n is the number of control variables in the tuning space. Depending upon the specific laser, this could be a large amount of data to acquire. If the cost of each measured point is high, then reducing this data is important. In addition, even if the map could be described theoretically with a model, reality does not correspond well enough to the model for a calibration to be derived from it (see Figure 1).

Currently, mapping is done by splitting measurements into coarse and fine measurements, where the coarse measurement improvement is in speed (mostly applicable to fast-tuning tunable lasers, not slow-tuning).

General Description

The disclosed method creates a coarse model for the map with minimal measurements, then performs a search. There is a balance between the coarseness of the model and the speed of search (i.e. with a good prediction, how many steps will it take to find the correct n-values that correspond to the frequency-channel calibration). This works for a tunable laser in which tuning speed is the limiting measurement factor. Thus, the cost of high precision and high accuracy measurements are out-weighed by the cost of scanning time (i.e. increasing scan speed while sacrificing measurement accuracy is not a factor).

The disclosed method is shown above in Figure 2. The first step is to create a minimally usable model for the tuning map. This model returns the first predictor val...