Solid immersion lens applications for nanophotonic devices

Solid immersion lens (SIL) microscopy combines the advantages of conventional microscopy with those of near-field techniques, and is being increasingly adopted across a diverse range of technologies and applications. A comprehensive overview of the state-of-the-art in this rapidly expanding subject is therefore increasingly relevant. Important benefits are enabled by SIL-focusing, including an improved lateral and axial spatial profiling resolution when a SIL is used in laser-scanning microscopy or excitation, and an improved collection efficiency when a SIL is used in a light-collection mode, for example in fluorescence micro-spectroscopy. These advantages arise from the increase in numerical aperture (NA) that is provided by a SIL. Other SIL-enhanced improvements, for example spherical-aberration-free sub-surface imaging, are a fundamental consequence of the aplanatic imaging condition that results from the spherical geometry of the SIL. Beginning with an introduction to the theory of SIL imaging, the unique properties of SILs are exposed to provide advantages in applications involving the interrogation of photonic and electronic nanostructures. Such applications range from the sub-surface examination of the complex three-dimensional microstructures fabricated in silicon integrated circuits, to quantum photoluminescence and transmission measurements in semiconductor quantum dot nanostructures

Efficient optical pumping using hyperfine levels in 145^{145}Nd3+^{3+}:Y2_2SiO5_5 and its application to optical storage

Efficient optical pumping is an important tool for state initialization in quantum technologies, such as optical quantum memories. In crystals doped with Kramers rare-earth ions, such as erbium and neodymium, efficient optical pumping is challenging due to the relatively short population lifetimes of the electronic Zeeman levels, of the order of 100 ms at around 4 K. In this article we show that optical pumping of the hyperfine levels in isotopically enriched $^{145}$Nd$^{3+}$:Y$_2$SiO$_5$ crystals is more efficient, owing to the longer population relaxation times of hyperfine levels. By optically cycling the population many times through the excited state a nuclear-spin flip can be forced in the ground-state hyperfine manifold, in which case the population is trapped for several seconds before relaxing back to the pumped hyperfine level. To demonstrate the effectiveness of this approach in applications we perform an atomic frequency comb memory experiment with 33% storage efficiency in $^{145}$Nd$^{3+}$:Y$_2$SiO$_5$, which is on a par with results obtained in non-Kramers ions, e.g. europium and praseodymium, where optical pumping is generally efficient due to the quenched electronic spin. Efficient optical pumping in neodymium-doped crystals is also of interest for spectral filtering in biomedical imaging, as neodymium has an absorption wavelength compatible with tissue imaging. In addition to these applications, our study is of interest for understanding spin dynamics in Kramers ions with nuclear spin.Comment: 8 pages, 6 figure

Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis

INTRODUCTION: Image sharing technologies may reduce unneeded imaging by improving provider access to imaging information. A systematic review and meta-analysis were conducted to summarize the impact of image sharing technologies on patient imaging utilization. METHODS: Quantitative evaluations of the effects of PACS, regional image exchange networks, interoperable electronic heath records, tools for importing physical media, and health information exchange systems on utilization were identified through a systematic review of the published and gray English-language literature (2004-2014). Outcomes, standard effect sizes (ESs), settings, technology, populations, and risk of bias were abstracted from each study. The impact of image sharing technologies was summarized with random-effects meta-analysis and meta-regression models. RESULTS: A total of 17 articles were included in the review, with a total of 42 different studies. Image sharing technology was associated with a significant decrease in repeat imaging (pooled effect size [ES] = -0.17; 95% confidence interval [CI] = [-0.25, -0.09]; P < .001). However, image sharing technology was associated with a significant increase in any imaging utilization (pooled ES = 0.20; 95% CI = [0.07, 0.32]; P = .002). For all outcomes combined, image sharing technology was not associated with utilization. Most studies were at risk for bias. CONCLUSIONS: Image sharing technology was associated with reductions in repeat and unnecessary imaging, in both the overall literature and the most-rigorous studies. Stronger evidence is needed to further explore the role of specific technologies and their potential impact on various modalities, patient populations, and settings

Packages for Terahertz Electronics

In the last couple of decades, solid-state device technologies, particularly electronic semiconductor devices, have been greatly advanced and investigated for possible adoption in various terahertz (THz) applications, such as imaging, security, and wireless communications. In tandem with these investigations, researchers have been exploring ways to package those THz electronic devices and integrated circuits for practical use. Packages are fundamentally expected to provide a physical housing for devices and integrated circuits (ICs) and reliable signal interconnections from the inside to the outside or vice versa. However, as frequency increases, we face several challenges associated with signal loss, dimensions, and fabrication. This paper provides a broad overview of recent progress in interconnections and packaging technologies dealing with these issues for THz electronics. In particular, emerging concepts based on commercial ceramic technologies, micromachining, and 3-D printing technologies for compact and lightweight packaging in practical applications are highlighted, along with metallic split blocks with rectangular waveguides, which are still considered the most valid and reliable approach.119Ysciescopu

Towards Prediction of Non-Radiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields

Many emerging technologies depend on human’s ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from non-radiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from first principles because the description of a slow non-radiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semiempirical method of calculating the fluorescence quantum yield (Φfl) for molecular chromophores, which, based on machine learning, converts simple electronic energies computed using time-dependent density functional theory (TDDFT) into an estimate of Φfl. As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φfl’s are known, so as to provide a black-box method which can later predict Φfl’s for chemically similar chromophores that have not been studied experimentally. As a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC) – the electronic properties of at least one higher-lying electronic state (Sn or Tn) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum a distorted local minimum near an S0/S1 conical intersection (which governs IC) and the magnitude of the spin–orbit coupling (which governs ISC). The resulting Φfl’s are predicted with reasonable accuracy (±22%), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing non-radiative relaxations

Machine learning and disease prediction in obstetrics

Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice

A family of stereoscopic image compression algorithms using wavelet transforms

With the standardization of JPEG-2000, wavelet-based image and video compression technologies are gradually replacing the popular DCT-based methods. In parallel to this, recent developments in autostereoscopic display technology is now threatening to revolutionize the way in which consumers are used to enjoying the traditional 2-D display based electronic media such as television, computer and movies. However, due to the two-fold bandwidth/storage space requirement of stereoscopic imaging, an essential requirement of a stereo imaging system is efficient data compression. In this thesis, seven wavelet-based stereo image compression algorithms are proposed, to take advantage of the higher data compaction capability and better flexibility of wavelets. [Continues.

2D Detectors for Particle Physics and for Imaging Applications

The demands on detectors for particle detection as well as for medical and astronomical X-ray imaging are continuously pushing the development of novel pixel detectors. The state of the art in pixel detector technology to date are hybrid pixel detectors in which sensor and read-out integrated circuits are processed on different substrates and connected via high density interconnect structures. While these detectors are technologically mastered such that large scale particle detectors can be and are being built, the demands for improved performance for the next generation particle detectors ask for the development of monolithic or semi-monolithic approaches. Given the fact that the demands for medical imaging are different in some key aspects, developments for these applications, which started as particle physics spin-off, are becomming rather independent. New approaches are leading to novel signal processing concepts and interconnect technologies to satisfy the need for very high dynamic range and large area detectors. The present state in hybrid and (semi-)monolithic pixel detector development and their different approaches for particle physics and imaging application is reviewed