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增长，无线生态系统的能源消耗不断增加。目前，世界上所有通信网络的总耗电量估计为每年几千太瓦时，并且这个数字预计在未来几十年内将成倍增加。然而，无线通信中新采用的用于增加带宽的毫米波（mmW）无线电系统的能效远低于在较低频段运行的传统无线电系统。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，并通过人工智能（AI）实时实现进一步增强。效率优化。通过降低能耗，普遍部署基于宽带隙的高效毫米波系统将减轻无线网络的大量碳排放，并最终有助于无线行业向“净零”排放发展。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，以满足我国频谱可持续性的迫切需求。该计划的影响将通过综合教育工作和推广活动进一步扩大。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与AI相结合，大幅提高毫米波阵列系统的能效。辅助从单个元件到阵列系统级别的动态优化。首先，将开发多芯片组装和封装集成，将WBG芯片无缝嵌入封装基板，并将其与其他功能块和天线互连。开发的HI工艺还将涉及基于“喷淋式”微加工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被共同集成，为人工智能训练提供实时温度传感数据。其次，提出了一种新型宽带隙毫米波功率放大器架构，称为混合非对称负载调制平衡放大器（H-ALMBA）。这种新颖的架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变容二极管可重构性，使 PA 能够针对高度紧凑的毫米波封装系统中的严重环境波动进行自我修复，包括天线扫描阻抗、温度、机械变形和工艺变化。第三，将开发人工智能辅助控制器来动态调整功率放大器阵列的操作参数，其中通过空中观测链路闭合控制回路以获取主波束数据。在该控制方案中，将研究算子学习和多模态数据融合，以通过考虑各种观测参数的不同影响来学习控制策略。为了实现AI引擎的低功耗和低延迟实施，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AI辅助控制器。该奖项反映了NSF的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。