Scalable and efficient grating couplers on low-index photonic platforms enabled by cryogenic deep silicon etching.

Efficient fiber-to-chip couplers for multi-port access to photonic integrated circuits are paramount for a broad class of applications, ranging, e.g., from telecommunication to photonic computing and quantum technologies. Grating-based approaches are often desirable for providing out-of-plane access to the photonic circuits. However, on photonic platforms characterized by a refractive index ≃ 2 at telecom wavelength, such as silicon nitride or thin-film lithium niobate, the limited scattering strength has thus far hindered the achievement of coupling efficiencies comparable to the ones attainable in silicon photonics. Here we present a flexible strategy for the realization of highly efficient grating couplers on such low-index photonic platforms. To simultaneously reach a high scattering efficiency and a near-unitary modal overlap with optical fibers, we make use of self-imaging gratings designed with a negative diffraction angle. To ensure high directionality of the diffracted light, we take advantage of a metal back-reflector patterned underneath the grating structure by cryogenic deep reactive ion etching of the silicon handle. Using silicon nitride as a testbed material, we experimentally demonstrate coupling efficiency up to - 0.55 dB in the telecom C-band with high chip-scale device yield.

ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality.

Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the quality of healthcare. To avoid this problem, it must be possible for untrained healthcare professionals to record diagnostically useful ECGs so they can send the recordings to experts for diagnosis. The ECGAssess Python-based toolbox developed in this study provides feedback regarding whether ECG signals are of adequate quality. Each lead of the 12-lead recordings was classified as acceptable or unacceptable. This feedback allows people to identify and correct errors in the use of the ECG device. The toolbox classifies the signals according to stationary, heart rate, and signal-to-noise ratio. If the limits of these three criteria are exceeded, this is indicated to the user. To develop and optimize the toolbox, two annotators reviewed a data set of 1,200 ECG leads to assess their quality, and each lead was classified as acceptable or unacceptable. The evaluation of the toolbox was done with a new data set of 4,200 leads, which were annotated the same way. This evaluation shows that the ECGAssess toolbox correctly classified over 94% of the 4,200 ECG leads as either acceptable or unacceptable in comparison to the annotations.

Robustness versus evolvability: a paradigm revisited.

Evolvability is the property of a biological system to quickly adapt to new requirements. Robustness seems to be the opposite. Nonetheless many biological systems display both properties-a puzzling observation, which has caused many debates over the last decades. A recently published model by Draghi et al. [Nature 463, 353-355 (2010)] elegantly circumvents complications of earlier in silico studies of molecular systems and provides an analytical solution, which is surprisingly independent from parameter choice. Depending on the mutation rate and the number of accessible phenotypes at any given genotype, evolvability and robustness can be reconciled. Further research will need to investigate if these parameter settings adequately represent the range of degrees of freedom covered by natural systems and if natural systems indeed assume a state in which both properties, robustness and evolvability, are featured.