Advanced antenna measurement and circuit debugging using Near-Field methods

使用近场方法进行高级天线测量和电路调试

• 批准号: 561432-2020
• 负责人: MirzavandBoroujeni, Rashid
• 金额: $ 4.37万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722016
• 关键词: Advanced; antenna; measurement; circuit; debugging

The 5th generation mobile network (5G) operates at higher frequencies and data rates with new challenges in the evolution of the standard, mm-wave adoption, and controlling the time and cost of tests. Therefore, low-cost fast characterization and diagnosis methods are required to improve and certify the quality of products. These methods will contribute to the development of current and future wireless devices. Between various antenna measurement methods, Near-Field (NF) is widely used since it can be conducted in a small and controllable environment. NF data is mainly used for antenna parameters calculation or circuit diagnostics. The measured field close to the surface of a device under test (DUT) can be used to calculate the far-field radiation pattern of the DUT in free space or to detect manufacturing problems and tuning the system. However, the two main drawbacks of the NF evaluation are the required time to scan the whole radiative area of the DUT and the error of couplings between the DUT and probe in the measured data. In this project, a novel near-field measurement system would be developed to reduce the time and error by introducing the reconfigurable probes and machine learning (ML)-assisted error correction methods. The electronic reconfigurability of the probe and the correction algorithms would be employed to minimize the physical movements and to reduce distortions in the measured data, respectively. Furthermore, the system would be extended by ML-assisted algorithms to implement automated fault detection of antennas and circuits. This system is particularly suitable for 5G devices evaluation, calibration, and certification.The industrial Partner of this project, DVTEST Inc, is a wireless test company specializing in the design and production of RF test enclosures and wireless test systems. DVTEST would endeavor to enable prototyping and proofs of concept to commercialize and deploy this technology for the benefit of DVTEST customers and Canadians in general. Specifically, the developed fast 5G test system would empower the production and qualification lines by providing real-time fault detection and calibration services.

第五代移动的网络（5G）以更高的频率和数据速率运行，在标准的演进、毫米波的采用以及控制测试的时间和成本方面面临着新的挑战。因此，需要低成本的快速表征和诊断方法来提高和认证产品的质量。这些方法将有助于当前和未来无线设备的发展。在各种天线测量方法中，近场（NF）由于可以在小而可控的环境中进行而被广泛使用。NF数据主要用于天线参数计算或电路诊断。靠近被测器件（DUT）表面的测量场可用于计算DUT在自由空间中的远场辐射模式或检测制造问题和调整系统。然而，NF评估的两个主要缺点是扫描DUT的整个辐射区域所需的时间以及测量数据中DUT和探针之间的耦合误差。本研究将开发一种新型的近场测量系统，通过引入可重构探针和机器学习（ML）辅助误差校正方法来减少时间和误差。探头的电子可重构性和校正算法将分别用于最小化物理移动和减少测量数据中的失真。此外，该系统将通过ML辅助算法进行扩展，以实现天线和电路的自动故障检测。该系统特别适用于5G设备的评估、校准和认证。该项目的工业合作伙伴DVTEST Inc是一家专门设计和生产射频测试外壳和无线测试系统的无线测试公司。DVTEST将奋进实现原型设计和概念验证，以商业化和部署这项技术，为DVTEST客户和加拿大人的利益。具体而言，开发的快速5G测试系统将通过提供实时故障检测和校准服务，为生产和认证线提供支持。