Femtosecond pulse compression using a neural-network algorithm

被引：27
|
作者：
Farfan, Camille A. [1 ]
Epstein, Jordan [1 ]
Turner, Daniel B. [1 ]
机构：
[1] NYU, Dept Chem, New York, NY 10003 USA
来源：
OPTICS LETTERS | 2018年 / 43卷 / 20期
基金：
美国国家科学基金会;
关键词：
GENERATION;
D O I：
10.1364/OL.43.005166
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
A key requirement for femtosecond spectroscopy measurements is to compress the laser pulse to its transform-limited duration. In particular, for few-cycle laser pulses, the compression process is time-consuming using conventional algorithms that converge statistically. Here we show that machine learning can accelerate the process of pulse compression: we have developed an adaptive neural-network algorithm to control a deformable-mirror-based pulse shaper that converges 100x faster than a standard evolutionary algorithm. (C) 2018 Optical Society of America
引用
收藏
页码：5166 / 5169
页数：4
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