Jitter Decomposition Meets Machine Learning: 1D-Convolutional Neural Network Approach

Abstract

A novel method of jitter decomposition by 1 Dimension-convolutional neural network (1D-CNN) using jitter histogram points is proposed for decomposing in the time interval error (TIE) of oscilloscope. Unlike the traditional jitter decomposition method based on the dual-Dirac model and Gaussian mixture model (GMM), the proposed jitter decomposition can reduce complexity and improve accuracy. The proposed method consists of a four-layer 1D-convolutional layer and multi-layer perceptron (MLP). Experimental results show that the proposed approach can decompose the total jitter (TJ) into deterministic jitter (DJ) and random jitter (RJ) and is better than the traditional jitter decomposition method, i.e., GMM, Fast Fourier transform and time lag correlation (FFT+TLC). Besides, results show that the proposed method’s performance is better than 2D-PointCNN, PointRNN, CNN, and PointANN.