Wireless platforms for communications, tracking, and sensing have become ubiquitous in recent years. These devices are now expected to stream video and other high data rate content, requiring exponential increases in the over-the-air bandwidth. At the same time, wireless platforms are often small (and becoming smaller) relative to wavelengths that are useful for long range and over-the-horizon communication. One of the key challenges in the continued miniaturization of these devices is reducing the required footprint of the antenna. Unlike many electronic components that benefit from decreased size, antennas suffer fundamental limitations in gain, efficiency, system range, and bandwidth when their size is reduced below a quarter-wavelength.
In this seminar, I will present my ongoing research, which focuses on the development of a generalized framework for modeling antennas based on fundamental principles and the application of these models to form coherent design strategies for compact, high-performance antennas. By studying the eigenmodes of radiating structures, I developed a physics-based analytical framework for modeling small antennas. Demonstrating the power of this approach, I designed single- and multi-mode spherical antennas with bandwidths approaching the physical limits. Because of this fundamental design approach, these antennas are versatile, scaling in frequency, size, and input impedance. The antennas are fabricated using a direct-write process that deposits a silver nanoparticle ink conformal to curved surfaces. Near-optimal, conformal antennas can be printed on nearly any three-dimensional package with this fabrication technique. This capability allows us to embed RF components entirely within a package-integrated antenna to create small, but efficient wireless sensors and other mobile devices. Furthermore, the generalized framework we are developing has many possible applications in the broader field, including reconfigurable and multiple-input multiple-output (MIMO) antenna design and improved computational algorithms.