Novel Deep Learning approaches for Complex Random Telegraph Noise Analysis

Machine learning, especially deep learning, have been rapidly developed in recent ten years in various aspects, from computer vision to natural language processing, and offers a powerful tool for us to solve challenging tasks in wide applications. Upon this successful demonstration, we design a sequence analysis structure based on deep learning technology for efficient analysis protocol to investigate complex random telegraph noise signals (RTN). RTNs appear prevalent in many classical and quantum devices. In a traditional method, it is a big challenge to extract quantitative information of each trap from the RTN signals in the presence of white noise and to detect transition rates accurately for multiple traps. Here we overcome this challenge by building a sequence analysis model using a Wavenet structure, and we extract signal amplitudes and time constants of many trap signals with multiple states with high accuracies.