Applications In Semiconductor Manufacturing Research Articles

Direct laser patterning of ruthenium below the optical diffraction limit

We describe a method that can be used to produce ruthenium/ruthenium oxide patterns starting from a ruthenium thin film. The method is based on highly localized oxidation of a small surface area of a ruthenium film by means of exposure to a pulsed laser under ambient conditions. Laser exposure is followed by dissolution of the un-exposed ruthenium in a NaClO solution, which leaves the conductive, partially oxidized ruthenium area on the substrate. Spatially selective oxidation, material removal, and, by implication, patterning, are, therefore, achieved without the need for a photoresist layer. Varying the exposure laser parameters, such as fluence, focus diameter, and repetition rate, allows us to optimize the process. In particular, it enables us to obtain circular Ru/RuO2 islands with a sub-diffraction-limited diameter of about 500 nm, for laser exposure times as short as 50 ms. The capability to obtain such small islands suggests that heat-diffusion is not a limiting factor to pattern Ru by laser heating on a (sub-)micron scale. In fact, heat diffusion helps in that it limits the area where a sufficiently high temperature is reached and maintained for a sufficiently long time for oxidation to occur. Our method provides an easy way to produce metallic Ru/RuO2 (sub-)micron structures and has possible applications in semiconductor manufacturing.

Digital Twin-Enabled Modelling of a Multivariable Temperature Uniformity Control System

The use of a digital twin as an enabling technology for industry 4.0 provides control systems engineers with novel tools for modelling, designing, and controlling complex systems, providing a deep understanding of the physical asset based not only on its physics but also the real system’s response. It is particularly critical for uniformity temperature control applications, where providing a reasonable model of the system’s diffusion is always affected by the physical behavior of the system’s components required for heating, cooling, or power distribution. In this paper, a digital twin is used to represent a multivariable thermoelectric system employed for temperature uniformity distribution control with potential applications in semiconductor manufacturing. The modelling employs a five-step methodological framework consisting of the stages: target system definition, system description, multiphysics and data-driven simulation, behavioral matching, and implementation to represent the system’s temperature distribution accurately. The temperature distribution is measured using an infrared thermal camera to perform model behavioral matching on heating and cooling temperature uniformity applications. The obtained results indicated that using digital twins not only increases the accuracy of the system’s representation but can also provide the system with novel information that can be leveraged for the design and implementation of smart control systems.

Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into semiconductor manufacturing has revolutionized defect detection and yield improvement processes. AI and ML algorithms analyze vast amounts of data generated during fabrication to enhance quality control, reduce defects, and optimize production yields. This paper provides an overview of AI and ML applications in semiconductor manufacturing, focusing on their roles in defect detection methodologies, process optimization, and yield enhancement strategies. Case studies and current advancements illustrate the transformative impact of AI and ML technologies on semiconductor fabrication, highlighting their potential to drive future advancements in microelectronics.

A2Pb3[H2N(CH2COO)2]3Cl5 (A = K, Rb): Porous Crystals Constructed on [Pb3Cl5O6] Clusters with Comprehensive Nonlinear Optical Performance

AbstractNonlinear optical (NLO) crystals that can extend the frequency range of coherent light have significant applications in laser technology, semiconductor manufacturing, and lithography. Herein, two interesting NLO crystals, A2Pb3[H2N(CH2COO)2]3Cl5 (A = K, Rb), are rationally obtained. Their porous structures are constructed on [Pb3Cl5O6] clusters that possess large hyperpolarizability and polarizability anisotropy, and are arranged in a parallel manner. Remarkably, A2Pb3[H2N(CH2COO)2]3Cl5 (A = K, Rb) exhibits comprehensive NLO performance, including strong phase‐matching second‐harmonic generation (SHG) response, the widest bandgap among lead oxychloride NLO crystals, larger laser damage threshold than 127 MW cm−2, suitable birefringence of 0.11@1064 nm, high thermal stability, non‐deliquescence, as well as facile crystal growth. Theoretical calculations and structural analysis demonstrate that their strong SHG response is predominantly originated from the [Pb3Cl5O6] cluster. This study will provide a unique method for the discovery of new interesting NLO crystals in future.

Guest Editorial Special Section on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Guest Editorial Special Section on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Monitoring of manufacturing process using bayesian EWMA control chart under ranked based sampling designs

Control charts, including exponentially moving average (EWMA) , are valuable for efficiently detecting small to moderate shifts. This study introduces a Bayesian EWMA control chart that employs ranked set sampling (RSS) with known prior information and two distinct loss functions (LFs), the Square Error Loss function (SELF) and the Linex Loss function (LLF), for posterior and posterior predictive distributions. The chart's performance is assessed using average run length (ARL) and standard deviation of run length (SDRL) profiles, and it is compared to the Bayesian EWMA control chart based on simple random sampling (SRS). The results indicate that the proposed control chart detects small to moderate shifts more effectively. The application in semiconductor manufacturing provides concrete evidence that the Bayesian EWMA control chart, when implemented with RSS schemes, demonstrates a higher degree of sensitivity in detecting deviations from normal process behavior. Comparison to the Bayesian EWMA control chart using SRS, it exhibits a superior ability to identify and flag instances where the manufacturing process is going out of control. This heightened sensitivity is critical for promptly addressing and rectifying issues, which ultimately contributes to improved quality control in semiconductor production.

Reinforcement learning for process control with application in semiconductor manufacturing

Process control is widely discussed in the manufacturing process, especially in semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since Reinforcement Learning (RL) has shown great advantages in learning actions from interactions with a dynamic system, we introduce RL methods for process control and propose a new controller called RL-based controller. Considering the fact that most existing run-to-run (R2R) controllers mainly rely on a linear model assumption for the process input–output relationship, we first discuss theoretical properties of RL-based controllers based on the linear model assumption. Then the performance of RL-based controllers and traditional R2R controllers (e.g., Exponentially Weighted Moving Average (EWMA), double EWMA, adaptive EWMA, and general harmonic rule controllers) are compared for linear processes. Furthermore, we find that the RL-based controllers have potential advantages to deal with other complicated nonlinear processes. The intensive numerical studies validate the advantages of the proposed RL-based controllers.

Codes and Data for paper "Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing"

Codes and Data for paper "Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing"

Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Generating plasma using a strong magnetic field directly from solid metal

Efficient, low-temperature generation of plasma for semiconductor manufacturing, welding and other applications

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

AI-based 3D Metrology and Defect Detection of HBMs in XRM Scans

In this paper, we employ the latest developments in 3D semi-supervised learning to create cutting-edge deep learning models for 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductor scans. We illustrate our approach to locating the region of interest of High Bandwidth Memory (HBM) structures and their individual components and identifying various defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefits of contrastive learning in the data pre-selection for our detection model and multi-scale Mean-Teacher training paradigm in 3D semantic segmentation to achieve better performance compared to the state of the art. We also provide an objective comparison for metrology-based defect detection with a 3D classification network. Our extensive experiments have shown that our approach outperforms the state of the art by up to 16% on object detection and 7.8% on semantic segmentation. Our fully-automated custom metrology package shows a mean error of less than 2 [Formula: see text]m for key features such as bond line thickness and provides better defect detection performance than the direct 3D classification approach. Overall, our method achieves state-of-the-art performance and can be used to improve the accuracy and efficiency of a wide range of failure analysis applications in semiconductor manufacturing. Finally, we also increase the segmentation models flexibility and adaptability to new data. We propose a generic training strategy and a new loss function that reduces the training time by 60% and the required amount of data by 48% making the training process more efficient.

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Guest Editorial Process-Level Machine Learning Applications in Semiconductor Manufacturing

The constantly increasing availability of data, the rapid expansion in computational and storage capacities of information technology systems, and algorithmic advances in Machine Learning (ML) and Artificial Intelligence (AI) are making a huge impact in the manufacturing industry for improving efficiency, operations and throughput. The semiconductor industry, being one of the most data-intensive industries, has seen the diffusion of several ML-based technologies in recent years.

Ranking Features to Promote Diversity: an Approach Based on Sparse Distance Correlation

The improvement of sensing technology enables features of process variables to be collected during the fabrication of products. This article develops an automatic tool for process feature rankings based on these data. Based on the sensing data characteristics and the need of manufacturing system analysis, we propose two rules of the feature ranking scheme: assessing general dependency between each individual process feature and the quality variable, and satisfying a diversity rule. Specifically, we propose a feature ranking scheme based on the sparse distance correlation (SpaDC) that satisfies these two rules. Theoretical properties of the proposed algorithm are investigated. Simulation studies and two real-case studies from semiconductor manufacturing applications demonstrate that the SpaDC method ranks the features effectively given these two ranking rules. The supplementary materials for this article are available online.

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Call for Papers for Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Machine learning virtual SEM metrology and SEM-based OPC model methodology

Background: E-beam metrologies, both critical dimension scanning electron microscope metrology and defect scan metrology, have been playing a very critical role in gating patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analyses. However, the low e-beam metrology tool throughput makes it impossible to obtain SEM images for larger area. Monte Carlo-based SEM image simulations or other SEM image simulations require postlithography or postetch pattern three-dimensional structures as prerequisite, and the simulation speed is not sufficiently fast for full chip implementation. Aim: We aim to develop machine learning SEM models with sufficient accuracy and speed for full chip application in semiconductor manufacturing environment. Approach: We have proposed a virtual SEM metrology solution based on U-Net neural network with physics-based feature maps as model input. With information in aerial image space encoded properly, SEM images of both postlithography and postetch can be predicted accurately enough for practical applications using our proposed virtual SEM metrology models. Equipped with GPUs, the machine learning-based SEM image models are fast enough to make it possible to realize full chip SEM generation from post-OPC data. Results: Our machine learning SEM image models can predict SEM images with normalized cross correlation around 0.95 in reference to ground truth SEM images, each SEM image (512 × 512 in size) takes about 800 ms using single CPU, and the speed can be accelerated to about 10 ms with single GPU. Conclusions: Using U-Net structure and physics-based feature maps as model inputs, machine learning-based SEM image models can be developed. The models are sufficiently accurate and fast to find their applications in semiconductor manufacturing, and they can be used as independent model for OPC data verification or generate SEM images as reference for SEM defect scan metrology.