Deep Learning Based Teaching Auxiliary Technology of Circuit Design

It is very important to innovate teaching evaluation tools. In this paper, we build a circuit design rule library in the switching power supply, and exploit deep learning tools to establish an intelligent diagnostic network model for evaluating possible errors in the circuit design process. We also implement a preliminary evaluation system to make it easy for students to use. This method we proposed can provide reference and improve efficiency for students and teachers. Firstly, we take input/output capacitance and inductance as the research object to analyze the ripple changes of input current and output voltage in different states. Furthermore, we summarize three important circuit design rules and collect experimental data through the WEBENCH simulation tool. Then the fast Fourier transform (FFT) operation is performed on this data, and the corresponding phase spectrum is calculated. Additionally, characteristic waveforms with discernability are selected to construct circuit design rule library as training samples of a neural network. Finally, the intelligent diagnostic network model is proposed and trained offline. The result shows that the method is feasible to some extent.