We demonstrate tunable Schottky barrier height and record photo-responsivity in a new-concept device made of a single-layer CVD graphene transferred onto a matrix of nanotips patterned on n-type Si wafer. The original layout, where nano-sized graphene/Si heterojunctions alternate to graphene areas exposed to the electric field of the Si substrate, which acts both as diode cathode and transistor gate, results in a two-terminal barristor with single-bias control of the Schottky barrier. The nanotip patterning favors light absorption, and the enhancement of the electric field at the tip apex improves photo-charge separation and enables internal gain by impact ionization. These features render the device a photodetector with responsivity ( 3 A W − 1 for white LED light at 3 mW cm − 2 intensity) almost an order of magnitude higher than commercial photodiodes. We extensively characterize the voltage and the temperature dependence of the device parameters, and prove that the multi-junction approach does not add extra-inhomogeneity to the Schottky barrier height distribution. We also introduce a new phenomenological graphene/semiconductor diode equation, which well describes the experimental I–V characteristics both in forward and reverse bias.

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more practical but challenging scenario, the annotator may miss the ground-truth and provide a wrong candidate set, which is known as the noisy PLL problem. To remedy this problem, we propose the PiCO+ framework that simultaneously disambiguates the candidate sets and mitigates label noise. Core to PiCO+, we develop a novel label disambiguation algorithm PiCO that consists of a contrastive learning module along with a novel class prototype-based disambiguation method. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. To handle label noise, we extend PiCO to PiCO+, which further performs distance-based clean sample selection, and learns robust classifiers by a semi-supervised contrastive learning algorithm. Beyond this, we further investigate the robustness of PiCO+ in the context of out-of-distribution noise and incorporate a novel energy-based rejection method for improved robustness. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

In this work, we have successfully grown high quality epitaxial [ ]-Ga₂O₃ thin films on [ ]-Ga₂O₃ (100) and Al₂O₃(0001) substrates using pulsed laser deposition (PLD). By optimizing temperature and oxygen pressure, the best conditions were found to be 650–700^∘C and 0.5[ ]Pa. To further improve the quality of hetero-epitaxial [ ]-Ga₂O₃, the sapphire substrates were pretreated for atomic terraced surface by chemical cleaning and high temperature annealing. From the optical transmittance measurements, the films grown at 600–750^∘C exhibit a clear absorption edge at deep ultraviolet region around 250–275[ ]nm wavelength. High resolution transmission electron microscope (HRTEM) images and X-ray diffraction (XRD) patterns demonstrate that [ ]-Ga₂O₃(-201)//Al₂O₃(0001) epitaxial texture dominated the epitaxial oxide films on sapphire substrate, which opens up the possibilities of high power electric devices.