# Leveraging Machine Learning and a Photonic Integrated Chip for the Spectro-Temporal Tailoring of Supercontinuum Generation

Bruno Chaves, Jérémy Saucourt, Van Thuy Hoang, Alexis Bougaud, Brent Little, Sai Chu, David Moss, Roberto Morandotti, Vincent Couderc, Benjamin Wetzel

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Bruno Chaves, Jérémy Saucourt, Van Thuy Hoang, Alexis Bougaud, Brent Little, et al. Leveraging Machine Learning and a Photonic Integrated Chip for the Spectro-Temporal Tailoring of Supercontinuum Generation. 2025 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Jun 2025, Munich, Germany. 10.1109/CLEO/Europe-EQEC65582.2025.11111412. [hal-05240514]

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Submitted on 11 Sep 2025

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# Leveraging Machine Learning and a Photonic Integrated Chip for the Spectro-Temporal Tailoring of Supercontinuum Generation

Bruno P. Chaves¹, Jérémy Saucourt¹, Van Thuy Hoang¹, Alexis Bougaud¹, Brent E. Little², Sai T. Chu³, David J. Moss⁴, Roberto Morandotti⁵, Vincent Coulerc¹, Benjamin Wetzel¹

1. XLIM Research Institute, CNRS UMR 7252, Université de Limoges, Limoges 87060, France

2. QXP Technologies Inc., Xi'an, China

3. Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China

4. Optical Sciences Centre, Swinburne University of Technology and the Australian Research Council (ARC) Centre of Excellence in Optical Microcombs for Breakthrough Science, COMBS (CE230100006), Hawthorn, Victoria, Australia

5. Institut National de la Recherche Scientifique - Centre Energie Matériaux Télécommunications, Varennes J3X 1S2, Canada

Programmable photonic integrated circuits (PICs) [1] have recently gathered great interest due to their capacity to add flexibility and versatility in optical systems to a degree that is not generally possible with either free-space or fibered setups. In particular, it has recently been shown that such programmable PICs can be used to control the spectral profile of supercontinuum generation (SC) [2], which can provide extra flexibility to several applications such as microscopy, metrology and hyperspectral imaging [3]. However, in many applications, there is an interest not only in controlling the optical spectrum, but also in tailoring the temporal profile of different wavelength components [4]. Yet, a platform that provides such flexibility on SC spectro-temporal control remains elusive.

<figure><img src="image_2.png" /><figcaption>**Fig. 1** (a) Experimental setup. (b-c) Experimental spectrograms obtained from GA (b) and ANN-seeded GA (c) optimization strategies, highlighting desired patterns (temporal interleaving and spectral overlap) with green circles.</figcaption></figure>

Here, we experimentally demonstrate the formation of SCs with tailored spectro-temporal profiles, leveraging machine learning strategies for SC optimization. The setup is shown in Fig. 1(a), whose basis of operation lies in the use of an on-chip programmable delay line (PDL), which transforms a pulse from a femtosecond laser into a train of pulses with controllable temporal patterns. This pulse train is then amplified and sent through a highly nonlinear fiber (HNLF), generating reconfigurable SC. Spectro-temporal characterization is performed via an asynchronous X-FROG system [5] to monitor the generated SC spectrograms, as illustrated in the examples of Fig. 1(b,c). The spectrogram is used to evaluate the generated SC with respect to a cost function for each target spectro-temporal pattern. Optimization can thus be performed since different SCs can be generated by iteratively adjusting the control signals issued to the PDL. A genetic algorithm (GA) was first used for optimization: Fig. 1(b) shows the GA optimization result targeting a four-pulse pattern at 1700 nm, each separated by 60 ps. A good agreement is obtained with the desired pattern. However, while successful, the GA optimization procedure may take some time, since the (random) initial population can be far from the optimized solution. To overcome this limitation, we also show an alternative approach leveraging an artificial neural network (ANN) trained to identify the relationship between the PDL control signal and the output SC spectrogram. Once trained, the ANN can generate an initial GA population much closer to the desired pattern, greatly speeding up the overall optimization. In Fig 1(c), we show the result of such an ANN-seeded GA, targeting the temporal overlap of four different wavelengths: 1640 nm, 1670 nm, 1700 nm and 1730 nm. Once again, a good agreement is obtained with the target spectro-temporal but with a 15x speed-up compared to a standard GA with random population initialization. In this paper, we further compare optimization strategies and results, as well as their impact for on-demand SC formation with refined properties for demanding applications.

## References

[1] W. Bogaerts, et al., “Programmable photonic circuits,” Nature **586**, 207–216 (2020).

[2] B. Wetzel, et al., “Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting,” Nat. Commun. **9**, 4884 (2018).

[3] T. Sylvestre, et al., “Recent advances in supercontinuum generation in specialty optical fibers” JOSA-B, **38**, F90 (2021).

[4] V. T. Hoang, et al., “Optimizing supercontinuum spectro-temporal properties by leveraging machine learning towards multi-photon microscopy,” Front. Photon. **3**, 940902 (2022).

[5] R. Trebino, “Frequency-Resolved Optical Gating: The Measurement of Ultrashort Laser Pulses” (Springer, 2000).

This work received funding from his work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement 950618 (STREAMLINE). B.W. acknowledges the support of the French ANR through the OPTIMAL project (ANR-20-CE30-0004) and the Région Nouvelle Aquitaine (SPINAL project). This research benefited from the support of the Platinom platform, with funding from the European Union and the Nouvelle Aquitaine council under the PILIM program.