頻率解析光閘法(frequency-resolved optical gating, FROG)是一種廣泛用於超短脈衝雷射的量測方法。實驗上，使用一臂具延遲(delay)的干涉儀進行自我干涉，記錄延遲長度對應的頻譜構成的二維圖像，被稱為FROG trace。傳統上會使用迭代演算法(Iterative algorithm)，花費數秒以還原待測脈衝的資訊。在量測超短脈衝的超寬頻譜時，使用FROG會遭遇晶體的頻率響應問題，造成FROG trace在頻率軸上被裁切。以往這種被裁切的FROG trace只有極少數迭代演算法才能完成相位還原，並且有所限制。
本研究建立了一個基於卷積神經網路(Convolutional Neural Networks, CNN)的演算法，能夠以毫秒級的計算時間得到足夠可靠的脈衝資訊。亦能成功達成裁切FROG trace的相位還原，並得到與迭代演算法相比更好的統計結果。因此，此方法對於超寬光譜之脈衝的即時量測與監控提供了基礎。

Frequency-resolved optical gating (FROG) is a common mothed for retrieving the complex field of ultrashort pulse from experimentally taken spectrogram (i.e. FROG trace). Traditionally, a FROG trace is processed by some iterative algorithm(s), and takes several seconds to arrive at the solution. FROG trace would be spectrally truncated when the nonlinear crystal failed to provide sufficient phase-matching bandwidth particularly in supercontinuum pulse measurement. Only a few algorithms can work with truncated FROG trace, and is somehow limited.
In this work, we develop a convolution neural network based mothed to retrieve spectral phase from spectrally truncated FROG traces in only few milliseconds, while the pulse reconstruction remains acceptable. This mothed is promising for real-time characterizations of supercontinuum pulses.

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