19 results
Search Results
2. Guest Editorial Special Section on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing.
- Author
-
Fowler, John W., Kempf, Karl, and Monch, Lars
- Subjects
ARTIFICIAL intelligence ,MANUFACTURING processes ,SEMICONDUCTOR manufacturing ,INFORMATION technology ,SEMICONDUCTOR technology ,MACHINE learning - Abstract
The increasing availability of data, advances in computational and storage capacities of IT systems, and algorithmic advances in Artificial Intelligence (AI), especially Machine Learning (ML) combine to enable significant improvements in the efficiency, operations and throughput of manufacturing systems at the production level. The semiconductor industry is one of the most data-intensive industries and has seen increased use of AI-based technologies over the last few years. In order to develop effective AI-based technologies in the semiconductor manufacturing industry several issues have to be taken into account, including scalability, heterogeneity of data, and the need for interpretability. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains.
- Author
-
Chien, Chen-Fu, Ehm, Hans, Fowler, John W., Kempf, Karl G., Monch, Lars, and Wu, Cheng-Hung
- Subjects
ARTIFICIAL intelligence ,SUPPLY chains ,SEMICONDUCTORS ,SUPPLY chain disruptions ,RESEARCH personnel ,SEMICONDUCTOR manufacturing - Abstract
This is a panel paper that discusses the use of Artificial Intelligence (AI) technologies to address production and supply chain level problems in semiconductor manufacturing. We have gathered a group of expert semiconductor researchers and practitioners from around the world who have developed AI solutions for various semiconductor problems. This paper aims to provide their answers to an initial set of questions and provide an overview of the AI developments and empirical studies to make suggestions for future directions in this arena. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. A Model Averaging Prediction of Two-Way Functional Data in Semiconductor Manufacturing.
- Author
-
Kim, Soobin, Kwon, Youngwook, Kim, Joonpyo, Bae, Kiwook, and Oh, Hee-Seok
- Subjects
SINGULAR value decomposition ,EMISSION spectroscopy ,SEMICONDUCTOR manufacturing ,OPTICAL spectroscopy ,PREDICTION models ,REGRESSION analysis - Abstract
This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Scheduling a Real-World Photolithography Area With Constraint Programming.
- Author
-
Deenen, Patrick, Nuijten, Wim, and Akcay, Alp
- Subjects
CONSTRAINT programming ,PHOTOLITHOGRAPHY ,SETUP time ,MACHINE tools ,SCHEDULING - Abstract
This paper studies the problem of scheduling machines in the photolithography area of a semiconductor manufacturing facility. The scheduling problem is characterized as an unrelated parallel machine scheduling problem with machine eligibilities, sequence- and machine-dependent setup times, auxiliary resources and transfer times for the auxiliary resources. Each job requires two auxiliary resources: a reticle and a pod. Reticles are handled in pods and a pod contains multiple reticles. Both reticles and pods are used on multiple machines and a transfer time is required if transferred from one machine to another. A novel constraint programming (CP) approach is proposed and is benchmarked against a mixed-integer programming (MIP) method. The results of the study, consisting of a real-world case study at a global semiconductor manufacturer, demonstrate that the CP approach significantly outperforms the MIP method and produces high-quality solutions for multiple real-world instances, although optimality cannot be guaranteed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
6. Data Cleansing With Minimum Distortion for ML-Based Equipment Anomaly Detection.
- Author
-
Hsieh, Yun-Che, Chen, Chieh-Yu, Liao, Da-Yin, Lin, Kuan-Chun, and Chang, Shi-Chung
- Subjects
DATA scrubbing ,ELECTROSTATIC discharges ,SEMICONDUCTOR manufacturing ,MACHINE learning ,ENTROPY (Information theory) ,SEMICONDUCTOR devices - Abstract
Semiconductor manufacturing has been extensively exploiting machine-learning (ML) to process equipment sensory data (ESD) for near-real time anomaly detection (AD). ESD characteristics are highly diversified and data lengths vary among processing steps and cycles. Cleansing ESD with minimum distortion (CMD) to fit the fixed-length input requirement by ML-based AD is critical to AD effectiveness and is challenging. This paper presents a novel CMD method of four innovations: i) statistical mode-based equalization of step data lengths for the least number of step data length changes, ii) importance indicator value (IIV) of a data sample based on its relative difference with the subsequent sample, and iii) step data segmentation into groups based on samples of significant IIVs and the least-entropy-group-to-cleanse-first rule, and iv) cleansing the least IIV sample(s) in the selected group for step data length equalization. CMD application to ESD demonstrates its characteristics preservation property. Simulation experiments are on an integration of data cleansing with an unsupervised ML-based AD system, STALAD. Comparisons with two benchmark methods over AD scenarios of small-scale drifts and shifts show that CMD not only is superior in facilitating accurate detection by STALAD but also helps detect anomaly much earlier than using the two benchmarks. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
7. Guest Editorial Special section on the 2022 International Symposium on Semiconductor Manufacturing.
- Author
-
Moriya, Tsuyoshi
- Subjects
SEMICONDUCTOR manufacturing ,SEMICONDUCTOR devices ,SEMICONDUCTOR technology ,ARTIFICIAL intelligence ,MACHINE learning ,CURRENT good manufacturing practices - Abstract
Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Semi-Supervised Learning for Simultaneous Location Detection and Classification of Mixed-Type Defect Patterns in Wafer Bin Maps.
- Author
-
Lee, Hyuck, Lee, Jaehyun, and Kim, Heeyoung
- Subjects
SUPERVISED learning ,MANUFACTURING processes ,SEMICONDUCTOR manufacturing ,SEMICONDUCTOR defects ,CLASSIFICATION ,SEMICONDUCTOR devices - Abstract
Identifying the patterns of defective chips in wafer bin maps (WBMs) in semiconductor manufacturing processes is crucial because different defect patterns correspond to different root causes of process failures. Recently, mixed-type defect patterns (i.e., multiple defect patterns in a single wafer) have become increasingly common owing to the increased complexity of semiconductor manufacturing processes. Previous methods for classifying mixed-type defect patterns in WBMs focused on outputting only the class labels of the defect patterns and not their locations, although location information of the defect patterns is useful for tracking the root causes of failure and improving processes. Moreover, most previous methods used only labeled WBM data, although a larger quantity of unlabeled WBM data are more accessible because of the costly process of label annotation. Therefore, in this paper, we propose a semi-supervised learning method for classifying mixed-type defect patterns and detecting their locations simultaneously using both labeled and unlabeled WBM data. The proposed method extends a recent unsupervised object detection method called Attend-Infer-Repeat in a semi-supervised manner to perform object detection and classification simultaneously. The performance of the proposed method is verified using WBM datasets of different sizes. The results demonstrate the effectiveness of the proposed method for classification and location detection. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
9. Sequential Residual Learning for Multistep Processes in Semiconductor Manufacturing.
- Author
-
Lee, Gyeong Taek, Lim, Hyeong Gu, and Jang, Jaeyeon
- Subjects
SEQUENTIAL learning ,MANUFACTURING processes ,SEMICONDUCTOR manufacturing ,CONCEPT learning ,LEARNING modules - Abstract
Semiconductor manufacturing consists of multiple sequential processes. In addition, even in a single process, a wafer must pass several steps. Accordingly, a dataset generated in semiconductor manufacturing has sequential information. Thus, the sequential information between steps and processes must be considered when predicting a target variable such as a yield or defect status. This paper proposes a method that utilizes the concept of residual learning to capture sequential information. Specifically, we propose to learn several modules that use data gathered starting from different steps and obtain a final decision by combining all modules’ decisions. In each module including multiple models, the first model is trained to predict the target variable using the data from the earliest step, and the remaining models are trained to predict the residuals that cannot be explained by the models for the previous steps based on the concept of residual learning. We conducted extensive experiments using two real-world semiconductor manufacturing datasets and found that even though each module’s performance was not good, the final decision obtained by combining all the modules’ decisions achieved a significant performance improvement. As a result, the proposed method significantly outperformed the baseline models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
10. A Practical Approach for Managing End-of-Life Systems in Semiconductor Manufacturing Using Health Index.
- Author
-
Patil, Deepak and Son, Stephen
- Subjects
SEMICONDUCTOR manufacturing ,MANUFACTURING processes ,CAPITAL investments ,PRODUCTION engineering ,SEMICONDUCTOR devices ,SUPPLY chains - Abstract
Equipment at the end of functional life poses several challenges for manufacturing operations and long-term asset sustainability. This is even critical for semiconductor manufacturing where equipment upgrades are capital intensive with a longer return horizon. This demands an objective and quantifiable approach to manage and monitor the end-of-life health of the manufacturing systems. The paper presents a practical three-level approach that brings together engineering, operational, and supply chain factors under a single indicator. A visualization heatmap correlates equipment health to manufacturing impact for engineering and commercial decision-making. The approach is simple yet proven to be effective in fab environments. The proposed analysis applies in formulating system priorities, upgrade strategies, and capital expenditures. Brief guidance on health improvement strategies, application to new fab, and cost-based evaluation are presented for practical use. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. Attention Mechanism-Based Root Cause Analysis for Semiconductor Yield Enhancement Considering the Order of Manufacturing Processes.
- Author
-
Lee, Min Yong, Choi, Yeoung Je, Lee, Gyeong Taek, Choi, Jongkwan, and Kim, Chang Ouk
- Subjects
SEMICONDUCTOR manufacturing ,MANUFACTURING processes ,ORDER picking systems ,ROOT cause analysis ,SEMICONDUCTORS ,MACHINE learning - Abstract
In semiconductor manufacturing processes, yield analysis aims to increase the yield by determining and managing the causes of low yield. The variable data collected from semiconductor manufacturing processes, in which hundreds of unit processes are implemented according to specific conditions and sequences, are interdependent, and the variables related to previous processes influence the variables in subsequent processes. Therefore, the order of processes should be considered when building a model that searches for the causes of low yield. However, there have been few studies in this area. This paper proposes a low-yield root cause search method considering the order of processes using a long short-term memory with attention mechanism (LSTM-AM) model. Specifically, the LSTM-AM model is applied to data classified according to the process structure of semiconductor products, and the causes of low yield are determined considering the order of processes by extracting attention weights. Experiments are conducted to verify the suitability of the proposed method using real yield data from a semiconductor company. The experimental results confirm that the proposed method outperforms the existing low yield root cause search methods in terms of low yield prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
12. An Autoencoder-Based Approach for Fault Detection in Multi-Stage Manufacturing: A Sputter Deposition and Rapid Thermal Processing Case Study.
- Author
-
Jebril, Hana T. T., Pleschberger, Martin, and Susto, Gian Antonio
- Subjects
RAPID thermal processing ,SPUTTER deposition ,SEMICONDUCTOR manufacturing ,FEATURE extraction ,SEMICONDUCTOR devices - Abstract
Data-driven Fault Detection and Classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-series, requiring the employment of time-consuming and possibly sub-optimal feature extraction approaches; (ii) the fact that faults/defects may be caused by more than a single process, but in many cases they are generated by a cascade of processes. In this paper, we tackle the first issue, by considering a two-stage case study consisting of a deposition process and a rapid thermal process. The proposed approach is based on convolutional deep autoencoders employed to perform feature extraction from time-series sensor data in frontend production equipment. We will show on the reported case study, how the proposed approach outperfoms key numbers-based approaches typically used in the industry. To allow reproducibility of the reported results and to foster research in the field, we publicly share the data used in this work. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
13. Gas-Delivery Fluid-Mechanical Timescales in Semiconductor Manufacturing.
- Author
-
Gonzalez-Juez, E.
- Subjects
SEMICONDUCTOR manufacturing ,MANUFACTURING processes ,ELECTRON tubes ,SEMICONDUCTOR devices ,FLUID dynamics ,FLUID flow - Abstract
Semiconductor manufacturing demands a fast delivery of multiple gases to the tool. Hence this document provides formulas for the fluid-mechanical timescales of this delivery. This is done with a simple but realistic model of a gas-supply system, together with theory and computational-fluid-dynamic (CFD) simulations, and for representative but not comprehensive conditions relevant to etch. This timescale analysis shows that the rate-limiting process is (i) convection in the MFC-manifold tubing or (ii) convection in the tube between the flow splitter and the process chamber. This depends on (a) the lowest MFC sccm in the gas-supply system and (b) the total gas-supply-system sccm. Therefore, speeding up the gas delivery requires enhancing (i) and (ii). Moreover, (i) would become more important in view of a current trend towards smaller MFC sccms in etch. Examples on how to speed up the gas delivery and enhance the mixing are provided. The present analysis can be adapted to other conditions and manufacturing processes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Advanced Process Control System for Trench Shape of Power Devices.
- Author
-
Ito, Takumi, Xueting, Wang, Oomuro, Yasuhisa, and Nagashima, Kazutaka
- Subjects
PROCESS control systems ,SEMICONDUCTOR manufacturing ,QUALITY control ,TRENCHES ,PROBLEM solving ,SEMICONDUCTOR devices ,BREAKDOWN voltage - Abstract
In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC) in which the shape of the processed feature is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual fab engineers, which cause difficulties in the QC. To overcome this problem, we have developed an automatic calculation method of the optimal recipe parameters with Advanced Process Control (APC) system with the stacking model method in order to solve these problems. In the development process, we have also performed comparison of two gap scoring methods. In the on-site demonstration of this system, we have further optimized the exploration method of the recipe parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
15. Discrete Active Disturbance Rejection Control for Semiconductor Manufacturing Processes With Dynamic Models.
- Author
-
Wang, Haiyan, Pan, Tianhong, and Chen, Guochu
- Subjects
MANUFACTURING processes ,DYNAMIC models ,SEMICONDUCTOR manufacturing ,TIME series analysis - Abstract
The carry-over effect is a common phenomenon in the semiconductor manufacturing process, giving the process a dynamic nature. Dynamic models are more accurate but with a consequent increase in uncertainty. Therefore, it is very important to eliminate the uncertainty and disturbance at the same time. To this end, a run-to-run (RtR) control scheme based on discrete active disturbance rejection control (DADRC) is proposed in this work. The process recipe is calculated using the state error feedback law, relying on the extended state observer (ESO) to effectively suppress the total disturbance synthesized by model uncertainty and external disturbance. Considering that tool aging often leads to drift disturbances in semiconductor manufacturing processes, a model-assisted ESO with two extended states is designed to estimate process states and total disturbance. Then an optimal observer gain is derived to minimize the estimation error. Finally, the numerical and industrial cases provide compelling evidence of the effectiveness of the proposed control scheme in suppressing tool-aging drift disturbance and a remarkable degree of tolerance towards uncertainties in the system model’s order and parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
16. Bayesian Nonparametric Classification for Incomplete Data With a High Missing Rate: an Application to Semiconductor Manufacturing Data.
- Author
-
Park, Sewon, Lee, Kyeongwon, Jeong, Da-Eun, Ko, Heung-Kook, and Lee, Jaeyong
- Subjects
MISSING data (Statistics) ,GAUSSIAN mixture models ,DATA distribution ,SEMICONDUCTOR manufacturing ,MANUFACTURING processes ,GAUSSIAN distribution - Abstract
During the semiconductor manufacturing process, predicting the yield of the semiconductor is an important problem. Early detection of defective product production in the manufacturing process can save huge production cost. The data generated from the semiconductor manufacturing process have characteristics of non-normal distributions, random missing patterns and high missing rate, which complicate the prediction of the yield. We propose the Dirichlet Process - Naive Bayes model (DPNB) that can simultaneously impute missing values and address classification problems. Since the DPNB is based on the infinite Gaussian mixture model, it can estimate complex data distributions and make predictions for missing datasets with some missing patterns due to nice properties of the Gaussian distribution. The DPNB also performs well for high missing rates since it uses all information of observed components. Experiments on various real datasets including semiconductor manufacturing data show that the DPNB has better performance than state-of-the-art methods in terms of predicting missing values and target variables as percentage of missing values increases. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
17. Vehicle Look-Ahead Dispatching for Overhead Hoist Transport System in Semiconductor Manufacturing.
- Author
-
Benzoni, Anna, Yugma, Claude, and Bect, Pierre
- Subjects
SEMICONDUCTOR manufacturing ,MANUFACTURING processes ,MIXED integer linear programming ,AUTOMATED materials handling - Abstract
This article investigates a look-ahead transport dispatching algorithm for the Automated Material Handling System (AMHS) of a semiconductor manufacturing facility. We consider two types of information: the estimated arrival time of upcoming transports of lots and the time required for an occupied vehicle to become idle. A Mixed Integer Linear programming model has been proposed to minimize the assignment (waiting) time of transport missions. Due to the complexity of the problem, three heuristics have been developed and tested on industrial data. The experiment simulation has been conducted by using a simulation model of a real semiconductor factory. The tests have shown the relevance and the effectiveness of the look-ahead dispatching algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
18. Streamlining Semiconductor Manufacturing of 200 mm and 300 mm Wafers: A Longitudinal Case Study on the Lot-to-Order-Matching Process.
- Author
-
Flechsig, Christian, Lohmer, Jacob, Lasch, Rainer, Zettler, Benjamin, Schneider, Germar, and Eberts, Dietrich
- Subjects
SEMICONDUCTOR manufacturing ,LONGITUDINAL method ,MANUFACTURING processes ,MATHEMATICAL optimization ,JOB satisfaction ,KEY performance indicators (Management) - Abstract
Lot-to-order matching (LTOM) is a crucial process in semiconductor manufacturing since inefficient allocation and order release have strong adverse effects on factory performance. Although prior research proposes several heuristics for the mathematical optimization of the LTOM process, successful real-world implementations following practical and comprehensive approaches are scarce. Our longitudinal case study addresses that issue by summarizing the results of an extensive research project on the automation and optimization of the LTOM process for 200 mm and 300 mm wafers at Infineon Technologies Dresden. Grounded in Action Design Research, we integrated different research methods to provide meaningful insights into the benefits, challenges, and best practices of our approach. Thereby, we also compare the results for 200 mm and 300 mm wafers. The project had positive impacts on multiple quantitative and qualitative key performance indicators, e.g., throughput, on-time delivery, tool utilization, cycle and working time savings, collaboration, and employee satisfaction. Finally, we provide managerial guidance for similar projects and implications for future research. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
19. Machine Learning-Based Process-Level Fault Detection and Part-Level Fault Classification in Semiconductor Etch Equipment.
- Author
-
Kim, Sun Ho, Kim, Chan Young, Seol, Da Hoon, Choi, Jeong Eun, and Hong, Sang Jeen
- Subjects
SEMICONDUCTOR manufacturing ,SUPPORT vector machines ,MANUFACTURING processes ,SEMICONDUCTORS ,MASS production ,CLASSIFICATION - Abstract
In the semiconductor manufacturing, which consists of significantly precise and diverse unit processes, minute defects can cause significantly large risk, which is directly related to the yield. Through fault detection and classification (FDC), the equipment status is monitored, and the potential causes of faults can be investigated. In the mass production process, unbalanced data problems are also important, including preprocessing methods for data analysis in real time. This study proposes a stepwise FDC method with a process fault detection (FD) and faulty equipment part classification. Fault detection (FD) is proposed using a one-class support vector machine (OC-SVM) to determine anomalies that occur during a process, and fault classification (FC) is followed by the importance between variables that determine whether a fault exists is extracted using extreme gradient boosting (XGBoost). Variables whose importance has been confirmed, are reclassified to a part-level based on the variable name, and defects are notified to the part-level level. An empirical study to validate the proposed data-based framework for fault detection and diagnosis was performed under the scenario of unexpected failure of two ${\mathbf {\mathrm {SF}}}_{\mathbf {6}}/{\mathbf {\mathrm {O}}}_{\mathbf {2}}$ mass flow controllers (MFCs). The experimental results confirmed that the application-oriented proposed framework performed well in FDC operations and showed that it can provide part-level notification to engineers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.