arXiv subjects

Nikhil Tiwale

Publications and source records attributed to Nikhil Tiwale.

4 recordsLinked to original sources

arXiv2026

Inverse mask design for interference lithography using automatic differentiable wave propagation

Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is formulated as an optimization problem, where the mask logits are updated through backpropagation of the loss between the simulated field amplitude and the target pattern. We optimize a mask that reproduces a target pattern with only 0.1% isolated pixel-level defects, resolving features at half the mask pixel pitch. To scale mask optimization, we employ the shifted ASM, which partitions the mask into patches that are propagated independently and summed at the image plane. For a 3.84 mm$\times$3.84 mm mask, shifted ASM with 16 patches reduces peak GPU memory by 3.8$\times$ at only 1.3$\times$ runtime cost relative to standard ASM. With gradient checkpointing, peak memory is reduced by 7.4$\times$ at 2$\times$ runtime. Distributing across multiple GPUs further accelerates the optimization. This work establishes a physics-informed, machine learning-driven approach for IL mask design, moving a step further towards complex, non-periodic patterns. The source code is available at https://github.com/chuntian236/holography-optimization.git .

physics.optics↗

arXiv2026

A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis

Scanning Electron Microscopy (SEM) is indispensable in modern materials science, enabling high-resolution imaging across a wide range of structural, chemical, and functional investigations. However, SEM imaging remains constrained by task-specific models and labor-intensive acquisition processes that limit its scalability across diverse applications. Here, we introduce the first foundation model for SEM images, pretrained on a large corpus of multi-instrument, multi-condition scientific micrographs, enabling generalization across diverse material systems and imaging conditions. Leveraging a self-supervised transformer architecture, our model learns rich and transferable representations that can be fine-tuned or adapted to a wide range of downstream tasks. As a compelling demonstration, we focus on defocus-to-focus image translation-an essential yet underexplored challenge in automated microscopy pipelines. Our method not only restores focused detail from defocused inputs without paired supervision but also outperforms state-of-the-art techniques across multiple evaluation metrics. This work lays the groundwork for a new class of adaptable SEM models, accelerating materials discovery by bridging foundational representation learning with real-world imaging needs.

cs.LG↗

arXiv2025

Accelerating Discovery of Solid-State Thin-Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment

Thin-film solid-state metal dealloying (thin-film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high-throughput characterization of thermal treatment parameters while probing local nanostructures of thin-film samples is needed. In this work, a laser-based thermal treatment is demonstrated to create temperature gradients on single thin-film samples of Nb-Al/Sc and Nb-Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X-ray multimodal and high-throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified within this framework. While the oxidation process may contribute to nanostructure formation during thin-film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. This approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin-film SSMD systems with targeted nanostructures.

cond-mat.mtrl-sci↗

arXiv2022

Charge Transport in Mixed Metal Halide Perovskite Semiconductors

Investigation of the inherent field-driven charge transport behaviour of 3D lead halide perovskites has largely remained a challenging task, owing primarily to undesirable ionic migration effects near room temperature. In addition, the presence of methylammonium in many high performing 3D perovskite compositions introduces additional instabilities, which limit reliable room temperature optoelectronic device operation. Here, we address both these challenges and demonstrate that field-effect transistors (FETs) based on methylammonium-free, mixed-metal (Pb/Sn) perovskite compositions, that are widely studied for solar cell and light-emitting diode applications, do not suffer from ion migration effects as their pure Pb counterparts and reliably exhibit hysteresis free p-type transport with high mobility reaching 5.4 $cm^2/Vs$, ON/OFF ratio approaching $10^6$, and normalized channel conductance of 3 S/m. The reduced ion migration is also manifested in an activated temperature dependence of the field-effect mobility with low activation energy, which reflects a significant density of shallow electronic defects. We visualize the suppressed in-plane ionic migration in Sn-containing perovskites compared to their pure-Pb counterparts using photoluminescence microscopy under bias and demonstrate promising voltage and current-stress device operational stabilities. Our work establishes FETs as an excellent platform for providing fundamental insights into the doping, defect and charge transport physics of mixed-metal halide perovskite semiconductors to advance their applications in optoelectronic devices.

cond-mat.mtrl-sci↗