PIN二极管,由于其结构简单，在高频大功率激励下具有可变电阻特性，通常在雷达前端用作限制器，以过滤高功率微波（HPM），以防止其电源进入内部电路并造成损坏。本文对PIN二极管的HPM效应进行了理论推导和研究,然后用优化的神经网络算法代替传统的物理建模来计算和预测PIN二极管限幅器的两类HPM限幅指标。我们针对以下两种预测场景中的每一种提出了神经网络模型：在不同HPM辐照下的时间-结温曲线的场景中，来自测试数据集的预测值与模拟值之间的加权均方误差(MSE)低于0.004。在预测PIN限制器的功率限制阈值时，插入损耗,以及不同HPM辐照下的最大隔离度，测试集预测值和模拟值的MSE均小于0.03。本研究提出的方法，应用优化的神经网络算法代替传统的物理建模算法来研究PIN二极管限幅器的高功率微波效应,显著提高了计算和仿真速度，降低了计算成本，为研究PIN二极管限幅器的高功率微波效应提供了一种新的方法。
PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter high power microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the HPM effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: in the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter\'s power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.
