基于数据处理及遗传算法建立HEMT Ⅰ-Ⅴ模型

Establishing Ⅰ-Ⅴ Model of HEMT Based on Data Processing and Genetic Algorithm

针对传统高电子迁移率晶体管(High electron mobility transistor, HEMT)非线性Ⅰ-Ⅴ模型需要根据脉冲Ⅰ-Ⅴ数据、直流Ⅰ-Ⅴ数据建立的现状,提出一种新的基于数据处理的方法,通过对直流Ⅰ-Ⅴ数据进行数据处理建立粗模型,利用遗传算法进行优化,建立完整的HEMT器件非线性Ⅰ-Ⅴ模型。上述方法基于Angelov模型,操作简单,节省了大量脉冲Ⅰ-Ⅴ测试的时间,所建立的模型精度高,适合HEMT器件建模,具有很好的实践意义。利用栅长为0.3 mm的pHEMT 25℃、85℃、125℃三个温度下直流Ⅰ-Ⅴ数据进行验证,所建立的模型与测试数据有较高的吻合性。

Aiming at the current situation that traditional high electron mobility transistor(HEMT) nonlinear Ⅰ-Ⅴ models need to be established based on pulsed Ⅰ-Ⅴ data and DC Ⅰ-Ⅴ data, a new data processing method is proposed, which performs data analysis on DC Ⅰ-Ⅴ data. Through data processing of DC Ⅰ-Ⅴ data, a rough model was established, and a genetic algorithm was used for optimization to establish a complete nonlinear Ⅰ-Ⅴ model of HEMT devices. This method is based on the Angelov model, which is simple to operate and saves a lot of time for pulsed Ⅰ-Ⅴ testing. The established model has high accuracy. It is suitable for HEMT device modeling and has good practical significance. The DC Ⅰ-Ⅴ data of pHEMT with grid length of 0.3 mm at three temperatures of 25°C,85°C and 125°C were used for verification. The model established has a good agreement with the test data.