A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm.

Area of Science:

• Electrical Engineering
• Materials Science
• Artificial Intelligence

Background:

• PIN diodes are crucial for protecting radar systems from high-power microwaves (HPM) due to their variable resistance.
• Traditional physical modeling for HPM effects on PIN diodes is computationally intensive and slow.

Purpose of the Study:

• To develop and validate an optimized neural network algorithm for predicting HPM effects on PIN diode limiters.
• To improve the efficiency and reduce the cost of simulating HPM effects in electronic components.

Main Methods:

• Theoretical derivation of HPM effects on PIN diodes.
• Development of a neural network model to predict time-junction temperature curves under HPM irradiation.
• Creation of a separate neural network model for predicting limiter performance indicators (threshold, insertion loss, isolation).

Main Results:

• The neural network model achieved a weighted mean squared error (MSE) below 0.004 for time-junction temperature predictions.
• MSE values were less than 0.03 for predicting PIN limiter's power limitation threshold, insertion loss, and isolation.
• The AI approach significantly improved computational and simulation speed.

Conclusions:

• Optimized neural networks offer a faster and more cost-effective alternative to traditional physical modeling for HPM effects on PIN diodes.
• This research provides a novel computational method for analyzing the high-power microwave behavior of PIN diode limiters.