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Jérémy Saucourt

Publications and source records attributed to Jérémy Saucourt.

3 recordsLinked to original sources

Spectro-temporal shaping of broadband optical wavepackets via programmable on-chip photonics

The ability to precisely shape the properties of light over multiple degrees of freedom constitutes the foundation of modern photonic architectures. Broadband sources with addressable spectro-temporal content are for instance critical for applications spanning biomedical imaging, material processing, lidar, and ultrafast spectroscopy, but also key enabling technologies in quantum information processing, high-capacity communications, and optical computing. Yet, flexible and adjustable spectro-temporal shaping so far remains a significant challenge, as it requires the simultaneous manipulation of both frequency and temporal domains across broad spectral bandwidths and extended timescales, from femtoseconds to nanoseconds. Importantly, conventional approaches to spectro-temporal processing typically rely on bulky and complex optical systems that lack scalability and flexibility, ultimately hampering their utility for applications requiring addressable multiphotonic processes. Here, we present a framework for spectro-temporal wavepacket control over a broad bandwidth, merging programmable integrated photonics with high-speed optical characterization. Leveraging machine leaning for tailoring nonlinear pulse propagation, we experimentally report scalable and on-demand shaping of broadband ultrafast pulse patterns. We demonstrate the efficacy of our method through reconfigurable power across 400 nm bandwidth with picosecond resolution, with promises for applications where precise control over light is paramount, in particular those needing versatile and controllable multiphoton excitations.

physics.optics↗

Machine learning-driven complex models for wavefront shaping through multimode fibers

We investigate a method to retrieve full-complex models (Transmission Matrix and Neural Network) of a highly multimode fiber (140 LP modes/polarization) using a straightforward machine learning approach, without the need of a reference beam. The models are first validated by the high fidelity between the predicted and the experimental images in the near field and far field output planes (Pearson correlation coefficient between 97.5% and 99.1% with our trained Transmission Matrix or Neural Network). Their accuracy was further confirmed by successful 3D beam shaping, a task achievable only with a true full complex model. As a prospect, we also demonstrate the ability of our neural network architecture to model nonlinear Kerr propagation in gradient index multimode fiber and predict the output beam shape.

physics.optics↗

Fast interrogation wavelength tuning for all-optical photoacoustic imaging

Optical detection of ultrasound for photoacoustic imaging provides large bandwidth and high sensitivity at high acoustic frequencies. Higher spatial resolutions can therefore be achieved using Fabry-Pérot cavity sensors, as compared to conventional piezoelectric detection. However, fabrication constraints during deposition of the sensing polymer layer require a precise control of the interrogation beam wavelength to provide optimal sensitivity. This is commonly achieved by employing slowly tunable narrowband lasers as interrogation sources, hence limiting the acquisition speed. We propose instead to use a broadband source and a fast tunable acousto-optic filter to adjust the interrogation wavelength at each pixel within a few microseconds. We demonstrate the validity of this approach by performing photoacoustic imaging with a highly inhomogeneous Fabry-Pérot sensor.

physics.optics↗