Determining the resolution limits of electron-beam lithography: direct measurement of the point-spread function.

One challenge existing since the invention of electron-beam lithography (EBL) is understanding the exposure mechanisms that limit the resolution of EBL. To overcome this challenge, we need to understand the spatial distribution of energy density deposited in the resist, that is, the point-spread function (PSF). During EBL exposure, the processes of electron scattering, phonon, photon, plasmon, and electron emission in the resist are combined, which complicates the analysis of the EBL PSF. Here, we show the measurement of delocalized energy transfer in EBL exposure by using chromatic aberration-corrected energy-filtered transmission electron microscopy (EFTEM) at the sub-10 nm scale. We have defined the role of spot size, electron scattering, secondary electrons, and volume plasmons in the lithographic PSF by performing EFTEM, momentum-resolved electron energy loss spectroscopy (EELS), sub-10 nm EBL, and Monte Carlo simulations. We expect that these results will enable alternative ways to improve the resolution limit of EBL. Furthermore, our approach to study the resolution limits of EBL may be applied to other lithographic techniques where electrons also play a key role in resist exposure, such as ion-beam-, X-ray-, and extreme-ultraviolet lithography.

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.

BACKGROUND: We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm. METHODS: We included all patients who completed 1 year of the study without reaching any primary endpoint during the first year, specifically: myocardial infarction, other acute coronary syndromes, stroke, heart failure or death from a cardiovascular event (n = 8799; 94%). In addition to clinical variables, features representing longitudinal SBP trends and variability were determined and combined in a random forest algorithm, optimized using cross-validation, using 70% of patients in the training set. Area under the curve (AUC) was measured using a 30% testing set. Finally, feature importance was determined by minimizing node impurity averaging over all trees in the forest for a specific feature. RESULTS: A total of 365 patients (4.1%) reached the combined primary outcome over 37 months of follow-up. The random forest classifier had an AUC of 0.71 on the testing set. The 10 most significant features selected in order of importance by the automated algorithm included the urine albumin/creatinine (CR) ratio, estimated glomerular filtration rate, age, serum CR, history of subclinical cardiovascular disease (CVD), cholesterol, a variable representing SBP signals using wavelet transformation, high-density lipoprotein, the 90th percentile of SBP and triglyceride level. CONCLUSIONS: We successfully demonstrated use of random forest algorithm to define best prognostic longitudinal SBP representations. In addition to known risk factors for CVD, transformed variables for time series SBP measurements were found to be important in predicting poor cardiovascular outcomes and require further evaluation.