Near-field spectroscopy of bimodal size distribution of InAs/AlGaAs quantum dots

We report on high-resolution photoluminescence (PL) spectroscopy of spatial structure of InAs/AlGaAs quantum dots (QDs) by using a near-field scanning optical microscope (NSOM). The double-peaked distribution of PL spectra is clearly observed, which is associated with the bimodal size distribution of single QDs. In particular, the size difference of single QDs, represented by the doublet spectral distribution, can be directly observed by the NSOM images of PL.Comment: 3pages, 3figue

Homotopy-based training of NeuralODEs for accurate dynamics discovery

Conceptually, Neural Ordinary Differential Equations (NeuralODEs) pose an attractive way to extract dynamical laws from time series data, as they are natural extensions of the traditional differential equation-based modeling paradigm of the physical sciences. In practice, NeuralODEs display long training times and suboptimal results, especially for longer duration data where they may fail to fit the data altogether. While methods have been proposed to stabilize NeuralODE training, many of these involve placing a strong constraint on the functional form the trained NeuralODE can take that the actual underlying governing equation does not guarantee satisfaction. In this work, we present a novel NeuralODE training algorithm that leverages tools from the chaos and mathematical optimization communities - synchronization and homotopy optimization - for a breakthrough in tackling the NeuralODE training obstacle. We demonstrate architectural changes are unnecessary for effective NeuralODE training. Compared to the conventional training methods, our algorithm achieves drastically lower loss values without any changes to the model architectures. Experiments on both simulated and real systems with complex temporal behaviors demonstrate NeuralODEs trained with our algorithm are able to accurately capture true long term behaviors and correctly extrapolate into the future.Comment: 12 pages, 6 figures, submitted to ICLR202

Optimization of Force Sensitivity in Q-Controlled Amplitude-Modulation Atomic Force Microscopy

We present control of force sensitivity in Q-controlled amplitude-modulation atomic force microscopy (AM-AFM) that is based on the high-Q quartz tuning-fork. It is found that the phase noise is identical to the amplitude noise divided by oscillation amplitude in AM-AFM. In particular, we observe that while Q-control does not compromise the signal-to-noise ratio, it enhances the detection sensitivity because the minimum detectable force gradient is inversely proportional to the effective quality factor for large bandwidths, which is due to reduction of frequency noise. This work demonstrates Q-control in AM-AFM is a useful technique for enhancement of the force sensitivity with increased Q or improvement of the scanning speed with decreased Q