arXiv subjects

Timothy van der Laan

Publications and source records attributed to Timothy van der Laan.

6 recordsLinked to original sources

arXiv2025

Intrinsically chiral exciton polaritons in an atomically-thin semiconductor

Photonic bound states in the continuum (BICs) have emerged as a versatile tool for enhancing light-matter interactions by strongly confining light fields. Chiral BICs are photonic resonances with a high degree of circular polarisation, which hold great promise for spin-selective applications in quantum optics and nanophotonics. Here, we demonstrate a novel application of a chiral BIC for inducing strong coupling between the circularly polarised photons and spin-polarised (valley) excitons (bound electron-hole pairs) in atomically-thin transition metal dichalcogenide crystals (TMDCs). By placing monolayer WS$_2$ onto the BIC-hosting metasurface, we observe the formation of intrinsically chiral, valley-selective exciton polaritons, evidenced by circularly polarised photoluminescence (PL) at two distinct energy levels. The PL intensity and degree of circular polarisation of polaritons exceed those of uncoupled excitons in our structure by an order of magnitude. Our microscopic model shows that this enhancement is due to folding of the Brillouin zone creating a direct emission path for high-momenta polaritonic states far outside the light cone, thereby providing a shortcut to thermalisation (energy relaxation) and suppressing depolarisation. Moreover, while the polarisation of the upper polariton is determined by the valley excitons, the lower polariton behaves like an intrinsic chiral emitter with its polarisation fixed by the BIC. Therefore, the spin alignment of the upper and lower polaritons ($\uparrow\downarrow$ and $\uparrow \uparrow$) can be controlled by $σ^+$ and $σ^-$ polarised optical excitation, respectively. Our work introduces a new type of chiral light-matter quasi-particles in atomically-thin semiconductors and provides an insight into their energy relaxation dynamics.

cond-mat.mes-hall↗

arXiv2025

Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact

Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data. While classical machine learning (CML) approaches have been successful in many domains, their performance degrades in small-sample, nonlinear scenarios. In this work, we investigate quantum machine learning (QML) as an alternative, exploiting quantum kernels to capture intricate correlations from compact datasets. Using only 159 experimental GaN HEMT samples, we develop a quantum kernel-aligned regressor (QKAR) combining a shallow Pauli-Z feature map with a trainable quantum kernel alignment (QKA) layer. All models, including seven baseline CML regressors, are evaluated under a unified PCA-based preprocessing pipeline to ensure a fair comparison. QKAR consistently outperforms classical baselines across multiple metrics (MAE, MSE, RMSE), achieving a mean absolute error of 0.338 Omega mm when validated on experimental data. We further assess noise robustness and generalization through cross-validation and new device fabrication. These findings suggest that carefully constructed QML models could provide predictive advantages in data-constrained semiconductor modeling, offering a foundation for practical deployment on near-term quantum hardware. While challenges remain for both QML and CML, this study demonstrates QML's potential as a complementary approach in complex process modeling tasks.

cs.LG↗

arXiv2024

Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays

The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabricate but crucial component in IoT systems, generate large volumes of data due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA) methods, a common solution to the data compression challenge, face limitations in preserving critical information during dimensionality reduction. In this study, we present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention. Our findings demonstrate that SAQK PCA outperforms cPCA in various back-end machine-learning tasks, especially in low-dimensional scenarios where access to quantum bits is limited. These results highlight the potential of noisy intermediate-scale quantum (NISQ) computers to revolutionize data processing in real-world IoT applications by improving the efficiency and reliability of CSA data compression and readout, despite the current constraints on qubit availability.

eess.SP↗

arXiv2024

Blue and Green-Mode Energy-Efficient Nanoparticle-Based Chemiresistive Sensor Array Realized by Rapid Ensemble Learning

The rapid advancement of Internet of Things (IoT) necessitates the development of optimized nanoparticle-based Chemiresistive Sensor (CRS) arrays that are energy-efficient, specific, and sensitive. This study introduces an optimization strategy that employs a rapid ensemble learning-based model committee approach to achieve these goals. Utilizing machine learning models such as Elastic Net Regression, Random Forests, and XGBoost, among others, the strategy identifies the most impactful sensors in a CRS array for accurate classification. A weighted voting mechanism is introduced to aggregate the models' opinions in sensor selection, thereby setting up two distinct working modes, termed "Blue" and "Green". The Blue mode operates with all sensors for maximum detection capability, while the Green mode selectively activates only key sensors, significantly reducing energy consumption without compromising detection accuracy. The strategy is validated through theoretical calculations and Monte Carlo simulations, demonstrating its effectiveness and accuracy. The employed optimization strategy elevates the detection capability of CRS arrays while also pushing it closer to theoretical limits, promising significant implications for the development of low-cost, easily fabricable next-generation IoT sensor terminals.

cs.LG↗

arXiv2023

Strong Light-Matter Coupling Facilitated Charge Carrier Transport in Cavity Organic Solar Cells

Strong light-matter coupling has shown great potential for modifying the electro-optical properties of semiconducting materials in recent years. In the strong coupling regime, excitons and cavity photons form new states named exciton-polaritons, with their properties a hybrid of each constituent. Herein, we report strong coupling observed in solution-processed donor:acceptor bulk-heterojunction organic solar cells (OSCs) evidenced by the observed Rabi splitting of ~300 meV and the effects of strong coupling on OSC operations. Combining the transient photovoltage decay measurement and nanosecond transient absorption spectroscopy, our results reveal that the effective charge carrier lifetimes are longer in cavity devices, attributed to the reduced bimolecular recombination. It is also found that access to CT state(s) of higher energy is enabled in cavity devices. This study demonstrates that strong coupling can effectively modify the device- and photo-physics in OSCs and opens a new pathway for engineering more efficient OSCs.

physics.app-ph↗

arXiv2023

Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

cs.LG↗