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Wide bandgap II-VI semiconductors, lattice-matched to InP substrates, show promise for use in novel, visible wavelength photonic devices; however, release layers for substrate removal are still under development. An under-etch method is reported which uses an InP substrate as an effective release layer for the epitaxial lift-off of lattice-matched ZnCdMgSe membranes. An array of 100-µm-square membranes is defined on a ZnCdMgSe surface using dry etching and suspended from the InP substrate using a three-step wet etch. The ZnCdMgSe membranes are transfer-printed onto a diamond heatspreader and have an RMS surface roughness < 2 nm over 400 µm², similar to the epitaxial surface. Membranes on diamond show a photoluminescence peak at ∼520 nm and a thermal redshift of 4 nm with ∼3.6 MWm⁻²continuous optical pumping at 447 nm. Effective strain management during the process is demonstrated by the absence of cracks or visible membrane bowing and the high brightness photoluminescence indicates a minimal non-radiative defect introduction. The methodology presented will enable the heterogeneous integration and miniaturization of II-VI membrane devices.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.