Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization

As semiconductor patterning dimensions shrink, more advanced Scanning Electron Microscopy (SEM) image-based defect inspection techniques are needed. Recently, many Machine Learning (ML)-based approaches have been proposed for defect localization and have shown impressive results. These methods often rely on feature extraction from a full SEM image and possibly a number of regions of interest. In this study, we propose a deep Reinforcement Learning (RL)-based approach to defect localization which iteratively extracts features from increasingly smaller regions of the input image. We compare the results of 18 agents trained with different feature extractors. We discuss the advantages and disadvantages of different feature extractors as well as the RL-based framework in general for semiconductor defect localization.Comment: 5 pages, 5 figures, 3 table

Guiding principles for the design of a chemical vapor deposition process for highly crystalline transition metal dichalcogenides

Two-dimensional transition metal dichalcogenides (TMDs) for advanced logic transistor technologies are deposited by various modifications of the chemical vapor deposition (CVD) method using a wide variety of precursors. Being a major electrical performance limiter, the TMD crystal grain size strongly differs between the various CVD precursor chemistries from nano- to millimeter-sized crystals. However, it remains unclear how the CVD precursor chemistry affects the nucleation density and resulting TMD crystal grain size. This work postulates guiding principles to design a CVD process for highly crystalline TMD deposition using a quantitative analytical model benchmarked against literature. The TMD nucleation density reduces favorably under low supersaturation conditions, where the metal precursor sorption on the starting surface is reversible and the corresponding metal precursor desorption rate exceeds the overall deposition rate. Such reversible precursor adsorption guarantees efficient long-range gas-phase lateral diffusion of precursor species in addition to short-range surface diffusion, which vitally increases crystal grain size. As such, the proposed model explains the large spread in experimentally observed TMD nucleation densities and crystal grain sizes for state-of-the-art CVD chemistries. Ultimately, it empowers the reader to interpret and modulate precursor adsorption and diffusion reactions through designing CVD precursor chemistries compatible with temperature sensitive application schemes

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

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

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

A route towards the fabrication of 2D heterostructures using atomic layer etching combined with selective conversion

Heterostructures of low-dimensional semiconducting materials, such as transition metal dichalcogenides (MX2), are promising building blocks for future electronic and optoelectronic devices. The patterning of one MX2 material on top of another one is challenging due to their structural similarity. This prevents an intrinsic etch stop when conventional anisotropic dry etching processes are used. An alternative approach consist in a two-step process, where a sacrificial silicon layer is pre-patterned with a low damage plasma process, stopping on the underlying MoS2 film. The pre-patterned layer is used as sacrificial template for the formation of the top WS2 film. This study describes the optimization of a cyclic Ar/Cl2 atomic layer etch process applied to etch silicon on top of MoS2, with minimal damage, followed by a selective conversion of the patterned Si into WS2. The impact of the Si atomic layer etch towards the MoS2 is evaluated: in the ion energy range used for this study, MoS2 removal occurs in the over-etch step over 1–2 layers, leading to the appearance of MoOx but without significant lattice distortions to the remaining layers. The combination of Si atomic layer etch, on top of MoS2, and subsequent Si-to-WS2 selective conversion, allows to create a WS2/MoS2 heterostructure, with clear Raman signals and horizontal lattice alignment. These results demonstrate a scalable, transfer free method to achieve horizontally individually patterned heterostacks and open the route towards wafer-level processing of 2D materials