【NextG联盟】20256G组件技术白皮书聚焦天线封装测试领域

6G Component Technologies: Antenna, Packaging, and Testing Summary

Core Content

The Next G Alliance Report provides an in-depth analysis of the challenges and emerging technologies related to antenna, packaging, and testing in the context of 6G wireless communication systems. It outlines the technological evolution required to support the high-frequency bands and advanced features of 6G, including beamforming, antenna integration in smartphones, antenna-on-display (AoD), and the use of machine learning and artificial intelligence (AI) in antenna design and simulation.

Main Viewpoints

• Antenna Challenges in 6G:

  • The integration of antennas in small form factor devices like smartphones becomes more complex due to the introduction of new frequency bands (e.g., 7.125–8.4 GHz, 14.8–15.35 GHz, and sub-THz bands).
  • Higher frequency bands suffer from increased path and penetration loss, necessitating the use of antenna arrays with higher beamforming gain.
  • Beamforming techniques (digital and analog) are critical for improving link performance and enabling high-speed communication.

• Antenna Integration in Smartphones:

  • Smartphones must support multiple frequency bands, including 2G, 4G, 5G, and 6G, which increases the number of antennas and antenna arrays required.
  • Isolation between antennas is a major concern, especially for lower frequency bands, where the physical size of the device limits the spacing between antennas.
  • The FR1 (low-band) and FR2 (high-band) frequency bands require different antenna configurations, such as single antenna arrays or multiple standalone antennas.

• Antenna-on-Display (AoD):

  • AoD is proposed as a solution to fit more antennas in the limited space of smartphones.
  • It involves integrating antennas on transparent films or within the display itself, which poses challenges such as maintaining transparency, touch sensitivity, and radiation efficiency.
  • The use of transparent metals like ITO can result in higher losses, so the design must balance these factors.

• Machine Learning and AI in Antenna Design:

  • ML and AI are being leveraged to reduce computational time and enhance design optimization.
  • Surrogate models are used to approximate EM simulations, allowing for faster and more efficient design processes.
  • These models can be trained on low-fidelity data to reduce simulation costs while maintaining accuracy.
  • Generative methods can automate and innovate antenna design by creating new geometries within constraints.

• Reconfigurable Intelligent Surfaces (RIS):

  • RIS is an emerging technology that enhances network coverage with low power and cost.
  • It involves reflective elements that can be dynamically reconfigured to improve signal propagation.
  • RIS is part of a broader category of metasurfaces, which are used for functionalities like anomalous reflection and polarization control.
  • Challenges include cost-effective phase control components (varactors, PIN diodes, RF MEMS) and flexible materials with good RF performance.

• Testing and Calibration:

  • Over-the-Air (OTA) testing for 6G will require high spatial resolution, accurate beam direction, and beam width.
  • New testing methodologies are needed to reduce cost and time, possibly incorporating AI/ML-based tools.
  • Built-in Self-Test (BIST) is recommended to lower test costs while maintaining accuracy.

Key Technologies and Innovations

• Digital Beamforming:

  • Enables MIMO and CA support by using precoding.
  • Offers higher directivity and better coverage at higher frequencies.

• Analog Beamforming:

  • Typically used for FR2 bands, with narrow beam width and limited MIMO support.
  • Requires beam sweeping to increase coverage and RF complexity.

• Heterogeneous Integration:

  • Combines silicon, III-V materials, and emerging technologies (e.g., GaN-on-SiC and GaN-on-Si) for optimal performance.
  • A holistic approach is necessary for the antenna-package-IC codesign.

• Sub-THz Frequency Bands:

  • Offer large bandwidths for high-speed data and high-fidelity sensing.
  • Pose technical challenges in codebook generation, beamforming, and array design.

• AI/ML in Antenna Design:

  • Reduces simulation time and resource usage.
  • Supports surrogate modeling, generative design, and optimization.

Key Recommendations

• Research and Development should focus on beamforming techniques, antenna integration, and AI/ML applications in antenna design.
• Standard organizations, silicon vendors, and test equipment vendors need to collaborate early to develop compatible and efficient test solutions.
• Heterogeneous integration and holistic design approaches should be prioritized to meet the performance and cost requirements of 6G.
• RIS and metasurfaces should be explored further for network coverage enhancement and signal propagation improvement.
• Antenna-on-display solutions require cross-disciplinary collaboration between display designers, manufacturers, and antenna engineers.

Conclusion

The report emphasizes the need for innovation and collaboration across various domains to address the technical and commercial challenges of 6G. It highlights the importance of antenna design in achieving high performance, cost efficiency, and reliability in next-generation wireless systems, while also pointing out the critical role of AI/ML in enabling faster and more efficient development processes. The integration of new technologies such as RIS and AoD is seen as essential for achieving the full potential of 6G.