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Jitter Decomposition by Convolutional Neural Networks

Jitter is one of the crucial factors used to characterize high-speed serial links and integrated circuit performance. Jitter decomposition is key tool with which to characterize jitter at a given bit error rate (BER). In this article, a novel jitter decomposition algorithm is proposed using convolutional neural networks to decompose random jitter (RJ) and deterministic jitter (DJ) by images of a jitter histogram, and predict the total jitter (TJ) at a BER of 10−12. The jitter histogram – that is, training data – is obtained by modeling the high-speed serial link in an advanced design system. The test results verify the feasibility and accuracy of the proposed method. The RJ mean absolute error (MAE) of the test is 0.8721 ps and the error rate 6.74%, and the DJ MAE is 4.8684 ps and the error rate 2.03%, and TJ MAE is 8.9406 ps and the average error rate 2.13%. In addition, results show that the effectiveness of the proposed method is better than most other jitter decomposition methods by evaluating the average error rate.