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

The University Of Central Florida Board Of Trustees, Orlando FL

Investigators

Abstract

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.