In order to comprehensively grasp the performance changes for the monolithic microwave integrated circuit (MMIC), this paper proposes that the complete temperature reliability tests for a 2.4–4.4 GHz gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) high gain power amplifier (PA) should be investigated. The performance for this MMIC PA at different temperatures has been presented effectively. The results show that the direct current (DC) characteristics, small-signal gain (S21), and radio frequency (RF) output characteristics for this MMIC PA decrease and the output third-order intersection point (OIP3) increases with the rising temperature. The main factor influencing the performance is analyzed in detail. For further applications of this MMIC PA, several measures can be utilized to remedy the performance degradation. This paper can provide significant engineer guidance for the reliability design of RF microwave circuits.

In order to investigate the temperature behavior for monolithic microwave integrated circuits (MMICs) under alpine conditions, the performance parameters of a 0.4–3.8 GHz gallium arsenide (GaAs) enhancement pseudomorphic high-electron-mobility transistor (E-pHEMT) low-noise amplifier (LNA) are tested at different temperatures. The typical temperatures of −39.2 °C, −32.9 °C, −25.3 °C, −11.3 °C, −4.9 °C, 0 °C and 23 °C are chosen as the alpine condition. The major performance indexes including the direct current (DC) characteristics, S-parameters, stability, radio frequency (RF) output characteristics, output third-order intersection point (OIP3) and noise figure (NF), which were inspected and analyzed in detail. The results show that the DC characteristics, small-signal gain (S21), RF output characteristics and NF all deteriorate with the rising temperature due to the decrease in two-dimensional electron gas mobility (). Contrary to this trend, the special design makes stability and OIP3 increase. For further application of this MMIC LNA under alpine conditions, several measures can be utilized to remedy performance degradation. This paper can provide some significant engineering value for the reliable design of MMICs.

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The nonlinear representation of active devices plays an important role in microwave circuit design. Whereas, it takes a long time to extract a large amount of large signal data, and the problem of memory resource and CPU occupancy becomes significant. In order to address the problems in traditional large-signal modeling methods, in this paper an X-parameter modeling method for microwave power devices based on extreme learning machine (ELM) is proposed. To demonstrate the effectiveness of this method, a double layer back propagation (BP) neural network model is established. Then, harmonic balance simulations are used to verify the accuracy of these two models. After comparisons, it is proved that the three harmonic errors of double layer BP neural network model are 9.525dBm, 1.309dBm and 14.593dBm, respectively, and the three harmonic errors of ELM model are 0.673 dBm, 0.314 dBm, 3.09 dBm, respectively. Furthermore, the three harmonic modulus errors of double layer BP neural network model are 0.031, 0.002, 7.665e-4, respectively, and the errors of ELM model are 0.005, 0.001, 8.38e-5, respectively. Finally, in order to verify the accuracy of the predicted model in circuit design, the predicted X-parameter is used in the design of power amplifier. Moreover, the errors of the double layer BP neural network prediction model at 2.5 GHz, 5 GHz and 7.5 GHz are 1.142 dBm, 1.436 dBm and 2.294 dBm, respectively. The output power error of the ELM model at 2.5 GHz, 5 GHz and 7.5 GHz are 0.089 dBm, 0.311 dBm and 0.309 dBm, respectively. These experimental results demonstrate that the established ELM model is an efficient and valid approach for modeling GaN high electron mobility transistor types of nonlinear microwave devices.

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