Abstract
Artificial neural networks are capable of fitting highly non-linear and complex systems. Such complicated systems can be found everywhere in nature, including the non-linear interaction between optical modes in laser resonators. In this work, we demonstrate artificial neural networks trained to model these complex interactions in the cavity of a Quantum Cascade Random Laser. The neural networks are able to predict modulation schemes for desired laser spectra in real-time. This radically novel approach makes it possible to adapt spectra to individual requirements without the need for lengthy and costly simulation and fabrication iterations.
Highlights
Classical simulations, usually based on assumptions and simplifications, are employed in order to map complex systems to an analytical model
In a digital experiment, where the physical component is replaced by a predictive model, no assumptions are required, which enables it to map the real world behavior, including effects that would be neglected in simulations
Optimization routine is used to enhance a specific mode of the original Quantum Cascade Random Lasers (QCRLs) spectrum by choosing a cost function costmax(k0) = 1/(max S(k0)), where S(k) is the predicted spectrum and k0 is the selected mode
Summary
Usually based on assumptions and simplifications, are employed in order to map complex systems to an analytical model. Machine learning based solutions in photonics are rapidly gaining momentum They are successfully used for controlling and modelling lasers [1,2], all-optical object recognition [3,4] and neuromorphic photonics [5,6]. In conjunction with the development of Terahertz (THz) Quantum Cascade Lasers (QCLs) it was for example recently shown that machine learning methods can complement and perhaps even replace the finite-elements method for certain problems [8]. Another area of photonics, where machine learning is already successfully used in ultra-fast photonics [9]
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