Jonathan Fan - US grants

Title: Exploring the limits of optical enhancement with nanoscale antennas

Non-technical description: Nanoscale antennas are a gateway to the next generation of miniaturized optical systems for communications, sensing, energy conversion, and optical imaging applications. The purpose of this research is to understand the physical limits of light manipulation with nanoscale metal antennas. In particular, our goal is to quantify these limits and to understand their connection with metal antenna geometry. We will probe the relationship between antenna geometry and optical properties by characterizing a metal antenna platform that can reconfigure as a function of mechanical strain. This research will help us develop new technologies, based on nanoscale optical devices, which can operate at their absolute physical limits. For example, it can be used to design ultra-sensitive sensing platforms with optical readout, which will serve as the cornerstone for new optically-based point-of-care biomedical technologies. It will also help us advance our understanding of green energy devices that utilize nanoscale antennas for photocatalytic and energy harvesting processes.

Technical description: Optical antennas have tremendous potential in miniaturized photonic systems because they can tailor electromagnetic fields with unprecedented control. The research goal of this NSF proposal is to measure and quantify plasmonic near-field coupling and field-enhanced phenomena in the classical and quantum regime using devices that can dynamically reconfigure with extreme mechanical control. Three research objectives will be pursued to: i) understand how optical modes transform in mechanically-actuated antennas; ii) explore the non-linear optics of antennas with nanoscale gap spacings; and, iii) understand how symmetry breaking can control optical modes. To address these objectives, a new type of mechanically-tunable nanoantenna, consisting of plasmonic antennas mounted onto an elastomer, is proposed. The project outcome will fill a significant void in the field of quantum plasmonics and address significant fundamental questions that are scientifically unexplored, including: what are the fundamental field-enhancement limits in coupled antennas? How can the chemical surface modification of antennas impact their near-field coupling in the quantum plasmonics regime? How can device reconfiguration enable extreme sensitivity in optical sensors? By analyzing individual antennas with dynamically tunable configurations, as oppose to multiple devices with differing static configurations, uncertainties due to uncontrolled fabrication variations from device to device are eliminated.

In contrast to legged robots inspired by locomotion in animals, this project explores robotic locomotion inspired by plant growth. Specifically, the project creates the foundation for classes of robotic systems that grow in a manner similar to vines. Within its accessible region, a vine robot provides not only sensing, but also a physical conduit -- such as a water hose that grows to a fire, or an oxygen tube that grows to a trapped disaster victim. The project will demonstrate vine-like robots able to configure or weave themselves into three-dimensional objects and structures such as ladders, antennae for communication, and shelters. These novel co-robots aim to improve human safety, health, and well-being at a lower cost than conventional robots achieving similar outcomes. Because of their low cost, vine robots offer exceptional educational opportunities; the project will include creation and testing of inexpensive educational modules for K-12 students.

This work broadens the concept of bio-inspired robots from animals to plants, the concept of locomotion from point-to-point movement to growth. In contrast to traditional terrestrial moving robots that tend to be based on the animal modality of repeated intermittent contacts with a surface, the vine modality begins with a root, harboring power and logic, and extends using growth, increasing permanent contacts throughout the process. This project will demonstrate a soft robot capable of growing over 100 times in length, withstanding being stepped on, extending through gaps a quarter of its height, climbing stairs and vertical walls, and navigating over rough, slippery, sticky and aquatic terrain. The design adopts a bio-inspired strategy of moving material through the core to the tip, allowing the established part of the robotic vine to remain stationary with respect to the environment. A thin-walled tube fills with air as it grows, allowing the vine robot to be initially stored in a small volume at its base, and to extend very large distances when controllably deployed. Mechanical modeling and new design tools will enable the development of task-specific vine robots for search and rescue, reconfigurable communication antennas, and construction. The paradigm of achieving movement and construction through growth will produce new technologies for integrated actuation, sensing, planning, and control; novel principles and software tools for robot design; and humanitarian applications that push the boundaries of collaborative robotics.

Nontechnical description: Noble metals are important materials in electronic and optical systems because they can conduct electrical currents with exceptional ease. Material defects strongly dictate the electronic, optical, thermal, and structural properties of these metals. To date, the precise role of defects in device performance is not well understood, in part because there do not exist robust and scalable ways to define defects in metallic structures. This research project aims to investigate the role of individual defects in the electronic and optical properties of gold devices. This work leverages a new technique for metal growth that specifies single defects in a simple and scalable way. An experimental understanding of how to control and characterize defects in noble metals provides new insights into how noble metal devices can be made to be more energy efficient, optically responsive, and mechanically robust. Experimental quantification of defect properties also enables theoretical researchers to more accurately model devices. The education component of this project targets engineering education at the high school level, by working with teachers in the lab to provide them with a research perspective in nanotechnology education, and by engaging with high school students through seminars and discussion.

Technical description: In this research project, the principle investigator explores methods to control the crystal orientation, grain boundary orientation, and grain boundary position in single- and bi-crystal gold metal microstructures. These material systems serve as model systems to explore the role of defects in active and passive plasmonic devices. A crystal growth technique called rapid melt growth specifies crystal orientation and defects in the metal. In this technique, a silica microcrucible encapsulates polycrystalline gold and a platinum seed, where the gold is first heated to its melting point and is then cooled. Liquid phase epitaxy initiates from the seed region, is directional, and specifies the crystal orientations of the metal microstructures based on the seed crystal. As such, this method serves as a versatile and scalable platform for defining the crystallographic properties of metals through lithographic patterning. For example, two seed structures at each end of a gold stripe produce bi-crystals with a range of tilt-boundary angles. The principle investigator aims to correlate parameters, such as tilt-boundary angle, with plasmon transport, using optical and electronic microscopy techniques. The education component of this project focuses on high school engineering education, by providing teachers with nanotechnology experience and students with summer laboratory programs and seminars.

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.

Device innovation today typically requires scientists or engineers to perform a time-consuming iterative procedure involving design and simulation. The design process is based on the application of expert knowledge to identify plausible device layouts, which are validated with a general physics-based algorithm and iteratively improved. New advances in machine learning have the potential to be disruptive in this innovation cycle, due to the ability for such algorithms to learn and process data in entirely new ways. This proposal focuses on the development of new machine learning tools that can automate and dramatically accelerate the design and simulation procedure by orders of magnitude faster speeds. These concepts will be based on a new class of algorithms that bring together conventional concepts in the data sciences with physics. Optical devices that can serve as miniaturized optical systems will be used as a testbed to benchmark the performance of these algorithms, though the concepts are ultimately general to scientific computing problems. If successful, these algorithms will serve as the foundation for a new class of computer-aided design tools that will help scientists and engineers innovate new classes of devices and systems with great expediency. The education goal of this project is to develop and disseminate new curricula that inspires high school students to consider STEM as a career pathway.


The research objective of this proposal is to create an algorithmic platform for the global optimization of freeform photonic devices that can scale to large area, three-dimensional, multi-functional devices. The fundamental roadblock that is targeted is the inability of existing global freeform optimization methods to practically scale to complex three-dimensional systems, due to scaling limits in the sampling and simulation of devices within the global design space. These fundamental scaling limits will be addressed by creating data-driven and physics-driven neural network electromagnetic surrogate solvers that can couple with new global search and design space evaluation tools based on deep network training. The proposed concepts will build on a recent discovery that population-based global freeform optimization can be performed by training a generative neural network using physics-based calculations. The expected outcomes are the development of new concepts and broadly applicable algorithms that will enable the global optimization of three-dimensional dielectric electromagnetic devices.

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.