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Particle accelerators represent an indispensable tool in science, healthcare, and industry. However, the size and cost of conventional radio-frequency accelerators limits the utility and reach of this technology. Dielectric laser accelerators (DLAs) provide a compact and cost-effective solution to this problem by driving accelerator nanostructures with visible or near-infrared (NIR) pulsed lasers, resulting in a factor of 10,000x reduction in scale. Current implementations of DLAs rely on free-space lasers directly incident on the accelerating structures, limiting the scalability of this technology due to the need of bulky optics and precise mechanical alignment. Therefore, integration with an inherently scalable architecture, such as photonic integrated circuits, is paramount to the development of an MeV-scale DLA for applications.
In this talk, I will present the demonstration of a waveguide-integrated DLA, designed using a photonic inverse design approach. I will first review the operation of DLAs and describe how one can formulate a figure-of-merit for the optimization of these structures. I will then briefly introduce the inverse design framework that allows for efficient free-form optimization of these structures, enabling search of a design-space that goes far beyond that of the tuning of a few geometric parameters. I will discuss the approaches we take to couple light to these devices before presenting the results of our single-stage on-chip integrated accelerator. I will conclude with the directions we are taking to reach higher on-chip acceleration gradients and energy-gain, including utilizing foundry fabrication for multi-stage accelerators.
Neil Sapra is a doctoral candidate in the Department of Physics at Stanford University. He received his B.S. in computational physics and theoeretical computer science from U.C. San Diego in 2015. His research focuses on developing laser coupling strategies for dielectric laser accelerators and demonstrating waveguide-integrated approaches to these accelerators. Both of these aims rely heavily on inverse design methodologies.
