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Multi-physical Field Collaborative Simulation Optimization Technology and Reliability Analysis of Power Amplifiers

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Artificial Intelligence and Machine Learning (IAIC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2058))

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Abstract

As the core RF front-end device of wireless communication systems, the performance of millimeter wave power amplifier chips determines the communication distance/quality of the system. In order to improve the communication distance of the system, power amplifiers are often required to operate in a saturated state, which can cause severe warping and deformation of PA chips due to high temperature accumulation. This seriously affects the performance of RF front-end, and has become a key issue that needs to be urgently solved in the industry. This paper takes MATLAB as the core to build a Circuit-Electromagnetic-Thermal-Force Multiple Physical Field collaborative simulation and optimization platform. In HFSS, using vbs script to achieve fully parameterized modeling and full band circuit electromagnetic joint simulation. In ANSYS Workbench, thermodynamic simulation is implemented using SCDM and Mechanical in IronPython environment. In this paper, a PA chip based on GaN process is simulated by using the Multi-Physical Field collaborative simulation and optimization platform. Aiming at its high temperature agglomeration problem, the number and layout of transistor count are optimized, and the maximum temperature is successfully reduced by about 70 ℃. In order to compensate for the circuit performance degradation caused by layout, this paper built an automatic optimization platform to optimize it, and the gain increased by about 8.4 dB compared with that before optimization, which proved the availability, reliability and progressiveness of the multi-physical field collaborative simulation optimization platform.

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Correspondence to Guangjun Wen .

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Li, Z. et al. (2024). Multi-physical Field Collaborative Simulation Optimization Technology and Reliability Analysis of Power Amplifiers. In: Jin, H., Pan, Y., Lu, J. (eds) Artificial Intelligence and Machine Learning. IAIC 2023. Communications in Computer and Information Science, vol 2058. Springer, Singapore. https://doi.org/10.1007/978-981-97-1277-9_20

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  • DOI: https://doi.org/10.1007/978-981-97-1277-9_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1276-2

  • Online ISBN: 978-981-97-1277-9

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