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As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too. [ABSTRACT FROM AUTHOR]

The 2022 ASMC, our 33rd, returned to Saratoga Springs, NY as an in-person conference after 2 years as a virtual conference. While we are all grateful for the digital world’s enhancements that allowed this conference to be held remotely, attendees were happy to return to an in-person event where networking is much easier. [ABSTRACT FROM AUTHOR]

This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

Beyond the widely-studied scheduling of wafers within cluster tools, a novel and important perspective is raised in this paper to tackle an upper-level optimization problem in real-world production, i.e., the assignment of hybrid types of wafer lots to a set of cluster tools with parallel modules to minimize the maximum completion time for the lots. The main difficulty in addressing such a problem is that the objective, i.e., the maximum completion time, cannot be calculated explicitly beforehand. To make this problem tractable, the associated maximal overlap among tools is utilized to heuristically evaluate the objective for the problem. Besides, since the cluster tools for processing are identical, we further tackle this problem as a clustering issue. Accordingly, a clustering algorithm based on greedy searching is proposed to allocate wafer lots into cluster tools while minimizing the maximal overlap. To elucidate our method and its significance in real-world production, the wet bench tool in wet cleaning process is taken as a case study. We compare the proposed algorithm with the empirical method in fabs and several intelligent optimization algorithms, and the experimental results verify the effectiveness of our proposed method in terms of improved efficiency. [ABSTRACT FROM AUTHOR]

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]

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]

Virtual metrology (VM) is crucial for improving process capability and production yield during semiconductor manufacturing processes. However, the performance of VM deteriorates owing to the variable operating regime and the nonlinear characteristics of the process. Herein, Variational inference Gaussian mixture model (VIGMM) and extreme learning machine (ELM) are combined to solve these issues. First, variational inference is conducted on a Gaussian mixture model to determine the number of Gaussian components automatically and the corresponding operating regimes are identified. Subsequently, an extreme learning machine is developed for each operating regime to investigate the nonlinear relationship between process inputs and outputs. Finally, VM is implemented using the corresponding local ELM, which is determined based on the responsibility of the Gaussian components. The feasibility and effectiveness of the proposed methods are validated based on a numerical case and the plasma sputtering process for fabricating thin-film transistor liquid-crystal displays. The proposed VIGMM-ELM can serve as a VM algorithm for manufacturing processes with multiple stages. [ABSTRACT FROM AUTHOR]

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]

In this case study, we introduce two ML techniques, Long Short-Term Memory (LSTM) and an optimized Random Forest (RF), to address challenges related to capacity and cost, by addressing problems of unscheduled downtime and Process Time (PT) variation in the case of a complex chamber processing tool. We show that by using these ML techniques, traditional methods of Predictive Maintenance (PdM) and PT analysis can be enhanced with new insights and lead to significant productivity improvements. We demonstrate that, with these methods, by detecting states and attributes of the tool, trends in the tool’s behavior can be more effectively identified to reduce its unscheduled downtime and improve its run-rate, thereby resulting in significant capacity and cost improvements. This is achieved by reducing the variability of availability; extending the Mean Time Between Failures (MTBF); and removing variability in PT between lots and chambers. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]