Publications

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This project will test the coupling of light emitted from silicon vacancy and nitrogen vacancy defects in diamond into additively manufactured photonic wire bonds toward integration into an "on-chip quantum photonics platform". These defects offer a room-temperature solid state solution for quantum information technologies but suffer from issues such as low activation rate and variable local environments. Photonic wire bonding will allow entanglement of pre-selected solid-state defects alleviating some of these issues and enable simplified integration with other photonic devices. These developments could prove to be key technologies to realize quantum secured networks for national security applications.

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The properties of materials can change dramatically at the nanoscale new and useful properties can emerge. An example is found in the paramagnetism in iron oxide magnetic nanoparticles. Using magnetically sensitive nitrogen-vacancy centers in diamond, we developed a platform to study electron spin resonance of nanoscale materials. To implement the platform, diamond substrates were prepared with nitrogen vacancy centers near the surface. Nanoparticles were placed on the surface using a drop casting technique. Using optical and microwave pulsing techniques, we demonstrated T1 relaxometry and double electron-electron resonance techniques for measuring the local electron spin resonance. The diamond NV platform developed in this project provides a combination of good magnetic field sensitivity and high spatial resolution and will be used for future investigations of nanomaterials and quantum materials.

We describe a method to automatically generate an ion implantation recipe, a set of energies and fluences, to produce a desired defect density profile in a solid using the fewest required energies. We simulate defect density profiles for a range of ion energies, fit them with an appropriate function, and interpolate to yield defect density profiles at arbitrary ion energies. Given N energies, we then optimize a set of N energy-fluence pairs to match a given target defect density profile. Finally, we find the minimum N such that the error between the target defect density profile and the defect density profile generated by the N energy-fluence pairs is less than a given threshold. Inspired by quantum sensing applications with nitrogen-vacancy centers in diamond, we apply our technique to calculate optimal ion implantation recipes to create uniform-density 1 μm surface layers of 15N or vacancies (using 4He).

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In this work, we present a new Li source for focused ion beam applications. Based on an AuSi eutectic alloy, Li is added as an impurity to minimize effects from degradation when exposed to air. We show the source is stable over the course of an hour and spot sizes ≲10 nm can be achieved. The Li beam can achieve hundreds of nanometer ranges in semiconductors with minimal damage being generated along the path length. The source performance is evaluated through a high-resolution ion beam induced charge collection experiment on an Si-based detector. Further application of the source for ion beam analysis is numerically explored; the example investigated is based on probing a semiconductor heterostructure through a Rutherford backscattering experiment, where the Li beam can reveal information that is inaccessible with either low energy or high energy He projectiles used as probes.

Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze 2D spectra to retrieve homogeneous and inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra with known linewidth values. The trained algorithm can analyze both simulated (not used in training) and experimental spectra to extract the homogeneous and inhomogeneous linewidths. This approach can be potentially applied to 2D spectra with more sophisticated spectral features.

Although analytical solutions exist, the analysis of two-dimensional spectroscopy (2DCS) data can be tedious. A machine learning approach to analyzing 2DCS spectra is presented. We test the accuracy of the algorithm on simulated and experimental data.

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The fundamental understanding of photoexcitation landscape and dynamics in hybrid organic-inorganic perovskites is essential for improving their performance in solar cells and other applications. The dual emission features from the orthorhombic phase in perovskites have been the focus of numerous recent studies, and yet their underlying molecular origin remains elusive. We use optical two-dimensional coherent spectroscopy to study the carrier dynamics and coupling of the dual emissions in a methylammonium lead iodide film at 115 K. The two-dimensional spectra reveal an ultrafast redistribution of the photoexcited carriers into the two emission resonances within 250 fs. The high-energy resonance is a short-lived transient state, and the low-energy emission state interacts with coherent phonons. The observed carrier dynamics provide important experimental evidence that can be compared with potential theoretical models and contribute to the understanding of the dual emissions as well as the overall energy level structure in hybrid organic-inorganic perovskites.

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