arXiv subjects

Li-Lin Tay

Publications and source records attributed to Li-Lin Tay.

5 recordsLinked to original sources

arXiv2026

Establishing the role of thickness in LaOBr; an alternative gate dielectric for vdW heterostructures

In electronic devices, the quality of the dielectric material is determined by figures of merit such as the band gap, leakage onset field, breakdown field and the number of built-in defects, which can all limit device performance. An additional and important metric to consider is the strength of the dielectric constant. Hexagonal boron nitride (hBN) is the benchmark gate dielectric for two-dimensional van der Waals heterostructures, owing to its wide bandgap, and atomically flat interface. At the same time, its dielectric constant (2--4) highlights the opportunity to explore alternative higher-permittivity van der Waals dielectrics that provide stronger electrostatic control. Previous work established lanthanum oxybromide (LaOBr), a layered dielectric with a predicted dielectric constant of 12.5, as a potential dielectric for van der Waals heterostructures. Using graphene field-effect devices with LaOBr, we show that the leakage-onset and breakdown fields decrease systematically with thickness. Dual-gate capacitance measurements combined with a dead-layer model reveal a bulk dielectric constant of $\varepsilon_{\mathrm{bulk}} = 12.2$, in agreement with theory, while interfacial dead-layers cause the effective dielectric constant to vary from 6 to 11 across multiple thicknesses. Benchmarked against established gate dielectrics, LaOBr combines breakdown fields comparable to hBN with dielectric constants 2 to 3 times larger. By establishing the operating regime of devices with various thicknesses, we further confirm the advantage of LaOBr over hBN for high-capacitance gate-dielectric operation. These results validate LaOBr as a promising dielectric for integration into two-dimensional heterostructures, and establish thickness-dependent benchmarking as essential for evaluating emerging layered dielectric materials.

cond-mat.mes-hall↗

arXiv2021

Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis

Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a raster pattern on a 0.5um grid. Unlike previously used unsupervised machine learning techniques such as principal component analysis, ICA is based on non-Gaussianity and statistical independence of data which is the case for mixture Raman spectra. Hence, ICA is a great candidate for assembling pseudo-colour maps from the spectral hypercube of Raman spectra. Our experimental results revealed that ICA is capable of reconstructing false colour maps of Raman hyperspectral data of human cells, showing the nuclear region constituents as well as subcellular organelle in the cytoplasm and distribution of mitochondria in the perinuclear region. Minimum preprocessing requirements and label-free nature of the ICA method make it a great unmixed method for extraction of endmembers in Raman hyperspectral maps of living cells.

q-bio.QM↗

arXiv2021

Raman spectral analysis of mixtures with one-dimensional convolutional neural network

Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application of 1-D CNNs has typically been restricted to binary classifications of pure compounds. Here we will highlight a new approach in spectral recognition and quantification of chemical components in a multicomponent mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose. The former is for rapid classification of components in a mixture while the latter is for quantitative determination of those constituents. In the proposed method, there is no limit to the number of compounds in a mixture. A data augmentation method is also introduced by adding random baselines to the Raman spectra. The experimental results revealed that the classification accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at the same time, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component.

cs.CV↗

arXiv2021

One-dimensional Active Contour Models for Raman Spectrum Baseline Correction

Raman spectroscopy is a powerful and non-invasive method for analysis of chemicals and detection of unknown substances. However, Raman signal is so weak that background noise can distort the actual Raman signal. These baseline shifts that exist in the Raman spectrum might deteriorate analytical results. In this paper, a modified version of active contour models in one-dimensional space has been proposed for the baseline correction of Raman spectra. Our technique, inspired by principles of physics and heuristic optimization methods, iteratively deforms an initialized curve toward the desired baseline. The performance of the proposed algorithm was evaluated and compared with similar techniques using simulated Raman spectra. The results showed that the 1D active contour model outperforms many iterative baseline correction methods. The proposed algorithm was successfully applied to experimental Raman spectral data, and the results indicate that the baseline of Raman spectra can be automatically subtracted.

eess.SP↗

arXiv2020

A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer

Raman spectroscopy is a powerful analytical tool with applications ranging from quality control to cutting edge biomedical research. One particular area which has seen tremendous advances in the past decade is the development of powerful handheld Raman spectrometers. They have been adopted widely by first responders and law enforcement agencies for the field analysis of unknown substances. Field detection and identification of unknown substances with Raman spectroscopy rely heavily on the spectral matching capability of the devices on hand. Conventional spectral matching algorithms (such as correlation, dot product, etc.) have been used in identifying unknown Raman spectrum by comparing the unknown to a large reference database. This is typically achieved through brute-force summation of pixel-by-pixel differences between the reference and the unknown spectrum. Conventional algorithms have noticeable drawbacks. For example, they tend to work well with identifying pure compounds but less so for mixture compounds. For instance, limited reference spectra inaccessible databases with a large number of classes relative to the number of samples have been a setback for the widespread usage of Raman spectroscopy for field analysis applications. State-of-the-art deep learning methods (specifically convolutional neural networks CNNs), as an alternative approach, presents a number of advantages over conventional spectral comparison algorism. With optimization, they are ideal to be deployed in handheld spectrometers for field detection of unknown substances. In this study, we present a comprehensive survey in the use of one-dimensional CNNs for Raman spectrum identification. Specifically, we highlight the use of this powerful deep learning technique for handheld Raman spectrometers taking into consideration the potential limit in power consumption and computation ability of handheld systems.

eess.SP↗