Modern design of photonic devices is quickly and steadily departing from classical geometries to focus more and more on non-trivial structures and metamaterials. These devices are governed by a multitude of parameters and the optimal design requires to simultaneously consider different figure of merits. In this invited talk we will present our recent work on the application of machine learning tools to the multi-objective optimization of multi-parameter photonic devices. In particular, we will demonstrate the potentiality of dimensionality reduction for the analysis of the complex design space of subwavelength metamaterials devices.
Exoplanetary biosignatures, molecular compounds which indicate a likelihood of extraterrestrial life, can be detected by highly sensitive spectroscopy of starlight which passes through the atmospheres of exoplanets towards the Earth. Such sensitive measurements can only be accomplished with the next generation of telescopes, leading to a corresponding increase in cost and complexity spectrometers. Integrated astrophotonic instruments are well-suited to address these challenges through their low-cost fabrication and compact geometries. We propose and characterize an integrated photonic gas sensor which detects the correlation between the near-infrared quasi-periodic vibronic absorption line spectrum of a gas and a silicon waveguide ring resonator transmittance comb. This technique enables lock-in amplification detection for real-time detection of faint biosignatures for reduced observation timescales and rapid exoplanetary atmosphere surveys using highly compact instrumentation.
Enabled by technological improvements, photonic devices and circuits are becoming increasingly more complex. Non-trivial geometries are designed to reduce device footprint, improve performance, and introduce novel functionalities. However, the number of design variables required to properly represent these geometries quickly grows, limiting the effectiveness of classical design approaches. Moreover, parameters are often strongly interdependent, restricting the use of sequential optimizations or independent parameter sweeps. Although several optimization techniques can be effective for multi-parameter design, they commonly allow to optimize for a single or a handful designs and the optimization process needs to be repeated if new performance criteria are introduced. In contrast to classical design approaches, the in uences of the design parameters remain hidden as well as the general behavior of the design space. In this paper we present an extension of our recent work on the application of machine learning pattern recognition to the design of multi-parameter photonic devices. In particular, we propose using a combination of local optimization based on the adjoint method and the use of dimensionality reduction. Adjoint optimization is used multiple times to generate a small set of different designs with high performance. Dimensionality reduction is applied to analyze the relationship between these degenerate designs and identify a lower-dimensional design sub-space that includes all alternative good designs. This sub-space can be mapped for any performance criteria thus enabling informed decisions based on the relative priorities of all relevant performance specifications. As a proof of concept, we demonstrate a ten-parameter design of an integrated photonic power splitter using silicon-on-insulator technology. We identify a region of possible high performance design solutions and select two design candidates either maximizing the splitter efficiency or minimizing back-reflection.
Astronomical instrumentation is traditionally costly, large, and alignment-sensitive owing to the use of bulk optics. The use of integrated photonic devices in astronomical instrumentation can mitigate such drawbacks in certain applications where high light throughput and spectral bandwidth are less crucial. In this work, we present an ultra-compact carbon dioxide detection scheme using a single silicon waveguide ring resonator. The comb-like absorption line spectrum of CO2 around 1580 nm wavelength can closely match the comb spectrum of an appropriately designed ring resonator. By actively correlating such a ring spectrum with the CO2 absorption lines, a specific detection signal can be generated. We design the free spectral range of a ring resonator to match the absorption line spacing of carbon dioxide lines in the range from 1575 to 1585 nm. Using thermo-optic modulation, the ring resonator drop or through port transmission spectrum can be shifted back and forth across the incoming CO2 light spectrum, resulting in a modulated signal with an amplitude proportional to the CO2 absorption line strength. Furthermore, high frequency modulation and lock-in detection can result in a significant improvement in the signal to noise ratio. We demonstrate that such a device can provide real-time carbon dioxide detection for applications in ground- and satellite-based astronomy, as well as remote atmospheric sensing, in a compact package. In future work, such a sensor can be adapted to a range of gases and used to determine radial velocities and compositional maps of astronomical objects.
In this paper, we present experimental results from site-selected single quantum dots that have
undergone a number of intermixing process steps via rapid thermal annealing. We show that the
intermixing process blueshifts the dot's emission spectrum without affecting the linewidth as well as
decreasing its biexciton binding energy and s-p shell spacing. The anisotropic exchange splitting is
shown to have undergone a sign inversion implying that the splitting had gone through zero.
Intermixing provides another nanoengineering tool for the design of scalable solid-state photon and
entangled photon pair sources.
Optoelectronic devices based on single, self-assembled semiconductor quantum dots are attractive for applications
in secure optical communications, quantum computation and sensing. In this paper we show how it is possible
to dictate the nucleation site of individual InAs/InP quantum dots using a directed self-assembly process, to
control the electronic structure of the nucleated dots and also how to control their coupling to the optical field by
locating them within the high field region of a photonic crystal nanocavity. For application within fiber networks,
these quantum dots are targeted to emit in the spectral region around 1550 nm.