An optimized DFT technology based on machine learning
Yang Han, Zeyu Zhao, Zhikuang Cai · 2021
During the DFT planning period, one of the design challenges is how to determine the core parameters of the DFT, so as to ensure the low cost, high efficiency and high reliability of the DFT. An optimized DFT technology based on machine learning is proposed in this paper. The method mainly includes four steps: data collection, optimal prediction model selection, parameter prediction, and optimal configuration calculation. This method can predict the results of all configurations in the selectable range with a small cost, thereby calculating the optimal DFT structure. The results of experiment show that the method can effectively calculate the optimal configuration of the design. For the most important test coverage parameter in DFT, the average prediction error is only 0.2767%.