Ms. Sneha Banerjee Profile

Sneha Banerjee

at Ctr. for Nano Science and Engineering (CeNSE)

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Proceedings Article | 4 March 2019 Paper

Adaptive pulse shaping for enhanced spectral broadening of high repetition rate, electro-optic frequency combs

B. S. Vikram, Roopa Prakash, Sneha Banerjee, Shankar Kumar Selvaraja, V. Supradeepa

Proceedings Volume 10902, 109020P (2019) https://doi.org/10.1117/12.2510117

KEYWORDS: Frequency combs, Electro optics, Modulators, Modulation, Pulse shaping, Optical fibers, Electrooptic modulators, Optical communications, Adaptive control

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High repetition rate frequency combs are predominantly used in optical communications, astronomical spectrographs and microwave photonics. Spectral broadening of electro-optic combs based on cascaded intensity and phase modulators with highly nonlinear fibers (HNLF) provides broadband combs with tunable repetition-rate and center-frequency. Spectral broadening is achieved using nonlinear effects such as self-phase modulation which requires substantial time dependent intensity at the input. To achieve this, the combs are compressed to a pulse using either fiber-based devices or pulse shapers. However, this has resulted in poor quality spectral broadening. Determining the optimal shaping profile of the input electro-optic comb for efficient spectral broadening is not direct due to the complex interplay between multiple parameters such as length, non-linear coefficient and dispersion of the nonlinear media, the initial spectral phases and power of the comb and modulator biasing conditions. This problem has been addressed here using adaptive pulse shaping. We use cascaded electro-optic modulators to generate a comb with 9 lines (within 20dB) around 1550nm at 25GHz repetition-rate. A wave-shaper changes the spectral phase of the comb. Dynamic spectral phase optimization by stochastic perturbations is performed in a closed loop by processing the output spectrum to maximize spectral bandwidth. With an output power of ~210mW, adaptive optimization more than tripled the number of lines to 29 (within 20dB) with a smooth spectral envelope while the unoptimized case causes negligible broadening (11 lines). We anticipate that the demonstrated testbed will enable more advanced methods of machine learning towards optimization and shaping of frequency combs.

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