Compact, broadband, and low-loss silicon photonic arbitrary ratio power splitter using adiabatic taper.

The arbitrary ratio power splitter is widely used in photonic integrated circuits (PICs), for signal monitoring, power equalization, signal feedback, and so on. Here we designed a fabrication-tolerant, compact, broadband, and low-loss arbitrary ratio power splitter. The proposed arbitrary ratio power splitter was realized with an adiabatically tapered silicon rib waveguide with 70 nm shallow etches and an Si3N4 waveguide. The fabrication analysis confirmed that both of them are robust to fabrication errors. 3D finite-difference time-domain simulations show a very low excess loss (less than 0.02 dB for Si3N4 waveguide and 0.05 dB for Si rib waveguide), and a broadband operating wavelength range (100 nm). Good fabrication tolerance and standard critical dimensions make the arbitrary ratio power splitter compatible with the standard fabrication process of commercial silicon photonic foundries.

Compact multimode waveguide based temperature-insensitive Mach-Zehnder interferometer.

A novel multimode waveguide based Mach-Zehnder interferometer (MZI) is demonstrated on an SOI platform with the properties of compact footprint and temperature-insensitive operation. The device can achieve a thermal dependence around 13pm/°C in a wavelength range of 40nm. Owing to the utilization of one single straight multimode waveguide, the device is naturally immune to local temperature distributions. The measured results exhibit transmissions with an extinction ratio better than 8dB and a minimum insertion loss lower than 0.31dB over the wavelength range of 1545nm-1585nm. Moreover, the proposed device is compatible with CMOS process.

Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery.

Objectives: Half of the patients who have tailored resection of the suspected epileptogenic zone for drug-resistant epilepsy have recurrent postoperative seizures. Although neuroimaging has become an indispensable part of delineating the epileptogenic zone, no validated method uses neuroimaging of presurgical target area to predict an individual's post-surgery seizure outcome. We aimed to develop and validate a machine learning-powered approach incorporating multimodal neuroimaging of a presurgical target area to predict an individual's post-surgery seizure outcome in patients with drug-resistant focal epilepsy. Materials and Methods: One hundred and forty-one patients with drug-resistant focal epilepsy were classified either as having seizure-free (Engel class I) or seizure-recurrence (Engel class II through IV) at least 1 year after surgery. The presurgical magnetic resonance imaging, positron emission tomography, computed tomography, and postsurgical magnetic resonance imaging were co-registered for surgical target volume of interest (VOI) segmentation; all VOIs were decomposed into nine fixed views, then were inputted into the deep residual network (DRN) that was pretrained on Tiny-ImageNet dataset to extract and transfer deep features. A multi-kernel support vector machine (MKSVM) was used to integrate multiple views of feature sets and to predict seizure outcomes of the targeted VOIs. Leave-one-out validation was applied to develop a model for verifying the prediction. In the end, performance using this approach was assessed by calculating accuracy, sensitivity, and specificity. Receiver operating characteristic curves were generated, and the optimal area under the receiver operating characteristic curve (AUC) was calculated as a metric for classifying outcomes. Results: Application of DRN-MKSVM model based on presurgical target area neuroimaging demonstrated good performance in predicting seizure outcomes. The AUC ranged from 0.799 to 0.952. Importantly, the classification performance DRN-MKSVM model using data from multiple neuroimaging showed an accuracy of 91.5%, a sensitivity of 96.2%, a specificity of 85.5%, and AUCs of 0.95, which were significantly better than any other single-modal neuroimaging (all p ˂ 0.05). Conclusion: DRN-MKSVM, using multimodal compared with unimodal neuroimaging from the surgical target area, accurately predicted postsurgical outcomes. The preoperative individualized prediction of seizure outcomes in patients who have been judged eligible for epilepsy surgery could be conveniently facilitated. This may aid epileptologists in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.