Real-time four-dimensional scanning transmission electron microscopy through sparse sampling

Four-dimensional scanning transmission electron microscopy(4-D STEM)is a state-of-the-art image acquisition mode used to reveal high and low mass elements at atomic resolution.The acquisition of the electron momenta at each real space probe location allows for various analyses to be performed from a single dataset,including virtual imaging,electric field analysis,as well as analytical or iterative extraction of the object induced phase shift.However,the limiting factor in 4-D STEM is the speed of acquisition which is bottlenecked by the read-out speed of the camera,which must capture a convergent beam electron diffraction(CBED)pattern at each probe position in the scan.Recent developments in sparse sampling and image inpainting(a branch of compressive sensing)for STEM have allowed for real-time recovery of sparsely acquired data from fixed monolithic detectors,Further developments in compressive sensing for 4-D STEM have also demonstrated that acquisition speeds can be increased,i.e.,live video rate 4-D imaging is now possible.In this work,we demonstrate the first practical implementations of compressive 4-D STEM for real-time inference on two different scanning transmission electron microscopes.

Surface defects incorporated diamond machining of silicon

This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the surface defect machining(SDM)method.The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating,cryogenic cooling,electric pulse or ultrasonic elliptical vibration.The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode.This paper made use of Raman spectroscopy data,average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional single-point diamond turning(SPDT)and SDM while incorporating surface defects in the(i)circumferential and(ii)radial directions.Complementary 3D finite element analysis(FEA)was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer.It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness(Ra)of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters.The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases.FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance.The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard,brittle materials such as SiC,ZnSe and GaAs as well as hard steels.

Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition

A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compounds.Its aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation energy.Here,we present a summary of the top-three ranked ML approaches.The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials.The second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression model.The third-place model employed the smooth overlap of atomic position representation with a neural network.The Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance.Ensembling relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.

All-optical tuning of a diamond micro-disk resonator on silicon

High-quality integrated diamond photonic devices have previously been demonstrated in applications from nonlinear photonics to on-chip quantum optics.However,the small sample sizes of single crystal material available,and the difficulty in tuning its optical properties,are barriers to the scaling of these technologies.Both of these issues can be addressed by integrating micrometer-scale diamond devices onto host photonic integrated circuits using a highly accurate micro-assembly method.In this work a diamond micro-disk resonator is integrated with a standard single-mode silicon-on-insulator waveguide,exhibiting an average loaded Q-factor of 3.1×10^4 across a range of spatial modes,with a maximum loaded Q-factor of 1.05×10^5.The micrometer-scale device size and high thermal impedance of the silica interface layer allow for significant thermal loading and continuous resonant wavelength tuning across a 450 pm range using a milliwatt-level optical pump.This diamond-on-demand integration technique paves the way for tunable devices coupled across large-scale photonic circuits.