ASCENT: Heterogeneously Integrated and AI-Empowered Millimeter-Wave Wide-Bandgap Transmitter Array towards Energy- and Spectrum-Efficient Next-G Communications

ASCENT：异构集成和人工智能支持的毫米波宽带隙发射机阵列，实现节能和频谱高效的下一代通信

• 批准号: 2328281
• 负责人: Kenle Chen
• 金额: $ 150万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328281&HistoricalAwards=false
• 关键词: ASCENT; Heterogeneously; Integrated; AI; Empowered

Due to the advent of data-intensive applications and rapid growth of communication networks, the energy consumption of wireless ecosystem has been ever-increasing. Currently, the total electricity consumption of all communication networks in the world is estimated to be a few thousand terawatt-hours per year, and this number is projected to be multiplied in the coming decades. Nevertheless, the millimeter-wave (mmW) radio systems newly adopted in wireless communications to increase the bandwidth are much less energy-efficient than the traditional radio systems operating at lower frequency bands. This project aims to fundamentally improve the energy efficiency of mmW systems by exploiting the highly efficient wide-bandgap (WBG) semiconductor technologies through heterogeneous integration (HI) and advanced packaging, which will be further enhanced by artificial intelligence (AI) for real-time efficiency optimization. By reducing the energy consumption, the ubiquitous deployment of high-efficiency WBG-based mmW systems will mitigate the massive carbon emission of wireless networks and eventually contribute to the evolution of wireless industry towards 'Net-Zero' emission. Moreover, this project will promote the capitalization of mmW frequency bands and ease the congestion of the sub-6-GHz spectrum to address the emergent need of spectrum sustainability in our nation. The impact of this program will be further expanded through integrated educational efforts and outreach activities.This project aims to vastly enhance the energy efficiency of mmW array system through advanced heterogeneous integration of high-power WBG power amplifier (PA) circuits in conjunction with AI-assisted dynamic optimization from individual elements to the array system level. First, multi-chip assembly and packaging integration will be developed to seamlessly embed WBG chips with the package substrate and interconnect them with other functional blocks and antennas. The developed HI process will also involve compatible cooling solutions based on a "shower-type" microfabricated nozzle structure to effectively manage the thermal condition of the WBG layer during system operation. Moreover, high-resolution thermal test structures will be co-integrated to provide real-time temperature sensing data for AI training. Second, a novel WBG mmW PA architecture, called hybrid asymmetrical load-modulated balanced amplifier (H-ALMBA), is proposed. This novel architecture offers unparalleled efficiency as well as high linearity. The H-ALMBA is also equipped with a powerful intrinsic varactor-less reconfigurability, enabling self-healing of the PA against severe environmental fluctuations in the highly compact mmW system in package, including antenna scan impedance, temperature, mechanical deformation, and process variation. Third, an AI-assisted controller will be developed to dynamically tune the operation parameters of the PA array, in which the control loop is closed via an over-the-air observation link for acquisition of the main-beam data. In this control scheme, operator-learning and multi-modal data fusion will be investigated to learn the control policy by considering the different impacts of various observation parameters. To achieve low-power and low-latency implementation of the AI engine, a hardware-friendly training framework will be developed to automatically compress and quantize the AI-assisted controller with high accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于数据密集型应用的出现和通信网络的快速发展，无线生态系统的能耗不断增加。目前，世界上所有通讯网络的总用电量估计为每年几千太瓦时，预计这一数字在今后几十年将成倍增加。然而，无线通信中新采用的毫米波（mmW）无线电系统以增加带宽，其能源效率远低于在较低频段工作的传统无线电系统。该项目旨在通过异构集成（HI）和先进封装，利用高效宽带隙（WBG）半导体技术，从根本上提高毫米波系统的能源效率，并将通过人工智能（AI）进一步增强实时效率优化。通过降低能源消耗，无处不在的基于wbg的高效毫米波系统的部署将减少无线网络的大量碳排放，并最终促进无线行业向“净零”排放的发展。此外，该项目将促进毫米波频段的资本化，缓解6ghz以下频谱的拥塞，以解决我国频谱可持续性的迫切需求。这一方案的影响将通过综合教育努力和外展活动进一步扩大。该项目旨在通过高功率WBG功率放大器（PA）电路的先进异构集成，结合人工智能辅助的从单个元件到阵列系统级的动态优化，大大提高毫米波阵列系统的能源效率。首先，将开发多芯片组装和封装集成，使WBG芯片与封装基板无缝嵌入，并与其他功能模块和天线互连。开发的HI工艺还将涉及基于“淋浴式”微制造喷嘴结构的兼容冷却解决方案，以有效管理系统运行期间WBG层的热状况。此外，高分辨率热测试结构将协同集成，为人工智能训练提供实时温度传感数据。其次，提出了一种新的WBG毫米波放大器结构，称为混合不对称负载调制平衡放大器（H-ALMBA）。这种新颖的结构提供了无与伦比的效率和高线性度。H-ALMBA还配备了强大的内在无可变因素可重构性，使PA能够在封装中高度紧凑的毫米波系统中应对严重的环境波动（包括天线扫描阻抗、温度、机械变形和工艺变化）进行自我修复。第三，将开发一个人工智能辅助控制器来动态调整PA阵列的操作参数，其中控制回路通过空中观测链路关闭，以获取主波束数据。在该控制方案中，将研究算子学习和多模态数据融合，以考虑不同观测参数的不同影响来学习控制策略。为了实现人工智能引擎的低功耗和低延迟实现，将开发一个硬件友好的训练框架，以高精度自动压缩和量化人工智能辅助控制器。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。