A new tool based on artificial neural networks to assist in the accurate positioning of the lenses used to collimate the beams emitted by the individual chips forming multi-emitter diode laser modules is presented. An alternative expression for the evaluation of the obtained beam quality is disclosed and the impact of different choices on the overall module performance in terms of beam quality and coupling efficiency into a collecting fiber is analyzed. Experimental validations with different combinations of lenses are reported to prove the effectiveness of the proposed approach.

Artificial neural network assisted laser chip collimator assembly and impact on multi-emitter module beam parameter product / Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido. - STAMPA. - 10085:(2017), pp. 1-7. (Intervento presentato al convegno Components and Packaging for Laser Systems III 2017 tenutosi a San Francisco (USA) nel Jan-Feb 2017) [10.1117/12.2254031].

Artificial neural network assisted laser chip collimator assembly and impact on multi-emitter module beam parameter product

YU, HAO;PERRONE, Guido
2017

Abstract

A new tool based on artificial neural networks to assist in the accurate positioning of the lenses used to collimate the beams emitted by the individual chips forming multi-emitter diode laser modules is presented. An alternative expression for the evaluation of the obtained beam quality is disclosed and the impact of different choices on the overall module performance in terms of beam quality and coupling efficiency into a collecting fiber is analyzed. Experimental validations with different combinations of lenses are reported to prove the effectiveness of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2674733
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