This paper proposes a dual‐band asymmetric Doherty power amplifier (ADPA) with 9 dB power back‐off range using dual parallel peaking amplifiers to realize reactance compensation for wideband operation at two frequency bands. To achieve dual‐band amplification, the specific phase delays of the carrier and peaking output matching networks (OMNs) required on the two target frequency bands were determined using the phase delay period repeatability principle combined with the Doherty impedance transformation first. Then, the carrier and peaking OMNs were designed according to the desired phase delay. More specifically, two parallel peaking amplifiers using the low‐order impedance transformation network were combined to generate the desired compensating reactance at the back‐off power range for wideband operation. For verification, a dual‐band ADPA operating over 2.35–2.75 and 3.2–3.6 GHz frequency bands was designed and measured. Experimental results show that, for the two frequency bands, the efficiencies at saturation are 53.6%–64.3% and 48%–67.8%, with the 9 dB back‐off efficiencies reaching 41.5%–53.3% and 40.5%–52.8%, respectively. When stimulated by a 20 MHz modulated signal, the designed ADPA can achieve the adjacent channel leakage ratio of −48 dBc after linearization.
In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch‐based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability of neural network. An image calibration algorithm is used for calibration preprocessing to enhance the robustness of the model. The Euclidean distance between a query image patch and its nearest normal patch is defined to be an anomaly score. For verification, three defects in the PA circuit, including components missing, solder contamination and component orientation dislocation, were detected. The experimental results show that, compared with conventional method, the area under receiver operating characteristic (AUROC) of the improved anomaly detection model increased from 90.1% to 95.1%, which improves the detection accuracy effectively.
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