Interfacial-antiferromagnetic-coupling driven magneto-transport properties in ferromagnetic superlattices

We explore the role of interfacial antiferromagnetic interaction in coupled soft and hard ferromagnetic layers to ascribe the complex variety of magneto-transport phenomena observed in $La_{0.7}Sr_{0.3}MnO_3/SrRuO_3$ (LSMO/SRO) superlattices (SLs) within a one-band double exchange model using Monte-Carlo simulations. Our calculations incorporate the magneto-crystalline anisotropy interactions and super-exchange interactions of the constituent materials, and two types of antiferromagnetic interactions between Mn and Ru ions at the interface: (i) carrier-driven and (ii) Mn-O-Ru bond super-exchange in the model Hamiltonian to investigate the properties along the hysteresis loop. We find that the antiferromagnetic coupling at the interface induces the LSMO and SRO layers to align in anti-parallel orientation at low temperatures. Our results reproduce the positive exchange bias of the minor loop and inverted hysteresis loop of LSMO/SRO SL at low temperatures as reported in experiments. In addition, conductivity calculations show that the carrier-driven antiferromagnetic coupling between the two ferromagnetic layers steers the SL towards a metallic (insulating) state when LSMO and SRO are aligned in anti-parallel (parallel) configuration, in good agreement with the experimental data. This demonstrate the necessity of carrier-driven antiferromagnetic interactions at the interface to understand the one-to-one correlation between the magnetic and transport properties observed in experiments. For high temperature, just below the ferromagnetic $T_C$ of SRO, we unveiled the unconventional three-step flipping process along the magnetic hysteresis loop. We emphasize the key role of interfacial antiferromagnetic coupling between LSMO and SRO to understand these multiple-step flipping processes along the hysteresis loop.Comment: 13 pages and 11 figure

SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15\% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed.Comment: 6 pages, 5 figures, To be published by IEEE in the proceedings of the 2023 ELMAR conferenc

Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE

The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30nm), necessary for the industrial implementation of High NA EUVL. Consequently, images from Scanning Electron Microscopy (SEM) suffer from reduced imaging contrast and low Signal-to-Noise Ratio (SNR), impacting the measurement of unbiased Line Edge Roughness (uLER) and Line Width Roughness (uLWR). Thus, the aim of this work is to enhance the SNR of SEM images by using a Deep Learning denoiser and enable robust roughness extraction of the thin resist. For this study, we acquired SEM images of Line-Space (L/S) patterns with a Chemically Amplified Resist (CAR) with different thicknesses (15nm, 20nm, 25nm, 30nm), underlayers (Spin-On-Glass-SOG, Organic Underlayer-OUL) and frames of averaging (4, 8, 16, 32, and 64 Fr). After denoising, a systematic analysis has been carried out on both noisy and denoised images using an open-source metrology software, SMILE 2.3.2, for investigating mean CD, SNR improvement factor, biased and unbiased LWR/LER Power Spectral Density (PSD). Denoised images with lower number of frames present unaltered Critical Dimensions (CDs), enhanced SNR (especially for low number of integration frames), and accurate measurements of uLER and uLWR, with the same accuracy as for noisy images with a consistent higher number of frames. Therefore, images with a small number of integration frames and with SNR < 2 can be successfully denoised, and advantageously used in improving metrology throughput while maintaining reliable roughness measurements for the thin resist

Tailoring the interfacial magnetic interaction in epitaxial La0.7_{0.7}Sr0.3_{0.3}MnO3_3/Sm0.5_{0.5}Ca0.5_{0.5}MnO3_3 heterostructures

Interface engineering in complex oxide heterostructures has developed into a flourishing field as various intriguing physical phenomena can be demonstrated which are otherwise absent in their constituent bulk compounds. Here we present La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) / Sm$_{0.5}$Ca$_{0.5}$MnO$_3$ (SCMO) based heterostructures showcasing the dominance of antiferromagnetic interaction with increasing interfaces. In particular, we demonstrate that exchange bias can be tuned by increasing the number of interfaces; while, on the other hand, electronic phase separation can be mimicked by creating epitaxial multilayers of such robust charge ordered antiferromagnetic (CO-AF) and ferromagnetic (FM) manganites with increased AF nature, which otherwise would require intrinsically disordered mixed phase materials. The origin of these phenomena is discussed in terms of magnetic interactions between the interfacial layers of the LSMO/SCMO. A theoretical model has been utilized to account for the experimentally observed magnetization curves in order to draw out the complex interplay between FM and AF spins at interfaces with the onset of charge ordering.Comment: 8 figure

Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.Comment: arXiv admin note: text overlap with arXiv:2206.1350

Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review

A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table

SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection

Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method

YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach

Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed self-assembly (DSA) for which no traditional, automatic defect inspection software exists. Machine Learning-based SEM image analysis has become an increasingly popular research topic for defect inspection with supervised ML models often showing the best performance. However, little research has been done on obtaining a dataset with high-quality labels for these supervised models. In this work, we propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns while requiring minimal quality control effort from a DSA expert. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset which best reflects DSA expert defect labeling expectations. We discuss the strengths and limitations of our proposed labeling approach and suggest directions for future work in data-centric ML-based defect inspection.Comment: 8 pages, 10 figures, accepted for the 38th EMLC Conference 202

Preparation and characterization of barium based perovskite dielectrics on different bottom electrodes by chemical solution deposition

Recently, there has been an interest in CSD techniques for the development of barium titanate (BT) based electrolytic capacitors, multi layer ceramic capacitors (MLCC), and embedded passives in printed wiring boards (PWB’s). In order to miniaturize these components further, the dielectric as well as the electrode thickness has to be reduced. Under such circumstances chemical solution deposition methods are increasingly being favoured for deposition of the dielectrics in the near future over other methods due to its simplicity and low precursor costs. While, the commercially available precursors for CSD are cheap, they have certain problems associated with them in terms of residual carbon content (specially under reducing atmospheres with base metal electrodes) and high processing temperatures. Another drawback of CSD based technique is the number of coatings required to achieve mesoscopic thicknesses (500 nm - 800 nm). It is the aim of this thesis to examine new methods of processing at lower temperatures with different bottom electrodes and achieve mesoscopic thicknesses in a few coating steps while maintaining acceptable device properties. The following processing changes were done in order to lower the crystallization temperature of barium-based perovskites. First, a more reactive atmosphere consisting of a mixture of ozone and oxygen was used to crystallize the films. At temperatures around 650oC the amorphous films derived from the carboxylate route were found to crystallize. Though such a process did not lower the crystallization temperature, post-annealing treatments in ozone reduce the leakage of the thin films by three orders of magnitude. In the second method, amorphous thin films of approximately 100 nm thickness were deposited on platinized silicon wafers and were subjected to different KrF laser fluences between 100 and 150 mJ/cm2. Though the crystallinity increased with increased laser fluences, irradiation above 150 mJ/cm2 led to ablation. Even on irradiating with lower fluences the dielectric films developed cracks during crystallization. Cracking was avoided by keeping the substrate at an elevated temperature of 250oC. This method can be used to crystallize thin films on different substrates where the substrate itself cannot be subjected to high temperature processing. Third, new precursor solutions based on aminoethoxides of barium and strontium were synthesized. By use of these carboxylate free precursors the formation of the intermediate oxo-carbonate phase was avoided. This method led to lower the crystallization temperature to 600oC. Both A-site and B-site substituted BT based thin films were fabricated on Ni electrodes. BT, BST and BTZ thin films of thickness around 600 nm were deposited by 12 multiple coatings. The pyrolysis and the crystallization procedures were optimized into 4 consecutive depositions followed by a crystallization step. This procedure was repeated thrice to achieve a thickness of 600 nm in 12 coating steps. Tunability and frequency dispersion for the different compositions was analysed with respect to processing temperature and post annealing treatments. The possibility to use CSD for the deposition of thin film dielectrics for future MLCC’s with thinner dielectric layers was shown. Finally a new method of deposition of hybrid solutions based on a mixture of microemulsions and CSD solutions (µECSD) was developed. The novelty of this method lies in the fact that films of mesoscopic thickness (500 nm - 800nm) can be deposited with only a few deposition steps. With these hybrid solutions such thicknesses can be achieved with only 5-8 steps depending on the amount of the MOD solution present in the hybrid solution