About

Dragan Djurdjanovic is a professor holding the Accenture Endowed Professorship in Manufacturing Systems Engineering at the University of Texas at Austin. His research interests include physical analytics, exploring the fusion of physics and data-driven models for modeling, monitoring, and control of complex systems, with applications in advanced manufacturing and human neuromusculoskeletal data analytics. His work also encompasses the fusion of physics and data analytics, condition monitoring, automatic process control, semiconductor manufacturing, and human body performance. He obtained his B.S. in Mechanical Engineering and Applied Mathematics in 1997 from the University of Nis, Serbia. He earned his M.S. in Mechanical Engineering from Nanyang Technological University, Singapore, in 1999, and his M.S. in Electrical Engineering (Systems) and Ph.D. in Mechanical Engineering from the University of Michigan, Ann Arbor, in 2002. His research includes advanced quality and process control in multistage manufacturing systems, intelligent proactive maintenance techniques, and applications of advanced data analytics in biomedical engineering. Dragan Djurdjanovic has served as the director of the NSF Industry-University Cooperative Research Center on Intelligent Maintenance Systems at The University of Texas at Austin and is the Associate Director of the NSF Engineering Research Center on Nanomanufacturing Systems (NASCENT Center). He has co-authored 69 journal publications, 9 book chapters, and over 30 refereed conference publications. He is a Fellow of the International Society for Engineering Asset Management, an Associate Member of the International Academy for Production Research (CIRP), and has received several awards, including the 2018 August-Wilhelm Scheer Visiting Professorship from the Technical University of Munich, the 2006 Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers, and the 2005 Teaching Incentive Award from the Department of Mechanical Engineering at the University of Michigan.

Research topics

• Computer Science
• Machine Learning
• Engineering
• Operations management
• Embedded system
• Manufacturing engineering
• Industrial engineering
• Business
• Reliability engineering

Selected publications

• Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model

  arXiv (Cornell University) · 2026-04-07

  preprintOpen accessSenior author

  Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among different individual machines which are nominally the same and are executing the same processes. To address the problem of detecting anomalies in a machine using sensory data gathered from different individual machines executing the same procedure, this paper proposes a cross-machine time-series anomaly detection framework that integrates a domain-invariant feature extractor with an unsupervised anomaly detection module. Leveraging the pre-trained foundation model MOMENT, the extractor employs Random Forest Classifiers to disentangle embeddings into machine-related and condition-related features, with the latter serving as representations which are invariant to differences between individual machines. These refined features enable the downstream anomaly detectors to generalize effectively to unseen target machines. Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines, confirming its effectiveness in enhancing cross-machine generalization.

  PublisherDOI

• Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model

  ArXiv.org · 2026-04-07

  articleOpen accessSenior author

  Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among different individual machines which are nominally the same and are executing the same processes. To address the problem of detecting anomalies in a machine using sensory data gathered from different individual machines executing the same procedure, this paper proposes a cross-machine time-series anomaly detection framework that integrates a domain-invariant feature extractor with an unsupervised anomaly detection module. Leveraging the pre-trained foundation model MOMENT, the extractor employs Random Forest Classifiers to disentangle embeddings into machine-related and condition-related features, with the latter serving as representations which are invariant to differences between individual machines. These refined features enable the downstream anomaly detectors to generalize effectively to unseen target machines. Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines, confirming its effectiveness in enhancing cross-machine generalization.

  PublisherOA PDF

• Robust multilayer control of critical overlay error components across a pattern layer in photolithography processes

  Journal of Process Control · 2026-03-26

  articleSenior authorCorresponding
  PublisherDOI

• Data-Driven Yield Estimation and Maximization Using Bayesian Optimization Under Uncertainty

  Applied Sciences · 2026-03-26

  articleOpen accessSenior author

  In this paper, we propose a novel method which utilizes samples of measured product quality characteristics to efficiently estimate the probabilities of those quality characteristics being within the desired specifications and, consequently, the process yield. Specifically, when dealing with 1D Gaussian distributions, we formally prove that the proposed yield estimator asymptotically gives a lower Mean Squared Error compared to the best unbiased estimator. In order to enable maximization of yield, this novel estimator is incorporated into the framework of Bayesian Optimization which iteratively seeks controllable tool parameters under which the outgoing product yield is maximized. The newly proposed yield maximization method is demonstrated in an application involving high-fidelity simulations of a reactive ion etch chamber, a tool component commonly used in semiconductor manufacturing. The aim of these simulations was to rapidly and reliably determine tool parameters that maximize the probability of delivering desired plasma density characteristics under stochastic variations in chamber conditions. The novel yield estimation and optimization methods show superiority when the number of experimental observations is limited and the distributions of outgoing product characteristics can be approximated well by a Gaussian distribution.

  PublisherDOI

• An approximate analytical method for the performance evaluation of semiconductor front-end fabrication with model-based inspection and rework policies in process control

  Journal of Manufacturing Systems · 2026-03-29

  articleOpen access

  Semiconductor front-end fabrication involves a highly complex and flexible job shop environment, prompting extensive research into modeling system performance and supporting efficient adaptive decision-making. Considerable attention has been directed toward photolithography, as it is considered the most critical processing step, with inspection processes directly influencing both the defect detection capability and overall productivity. Typically, to ensure precision, each wafer layer undergoes thorough inspection with measurement markers spread across the entire wafer surface, resulting in long inspection times. Recent studies have shown that model-based process control coupled with the optimal down-selection of measurement markers can significantly enhance system performance. Building on this insight, this study aims to expand the analysis of semiconductor front-end fabrication by proposing a novel analytical model for the evaluation of the steady-state performance of quality and productivity. The model accounts for the material flow split based on quality attributes: defective parts are either scrapped or sent to rework stations, whereas undetected defects continue through the line. In addition, it integrates the dynamics of the full front-end process chain, offers a comprehensive representation of the system, and supports informed inspection policy decisions. This model has been effectively used to optimize inspection strategies, enabling a balanced trade-off between quality control and system productivity. Furthermore, the approach is generalizable and applicable to other manufacturing contexts that face similar trade-offs between inspection effort, resulting quality levels and throughput.

  PublisherDOI

• Robust Multilayer Control of Critical Overlay Error Components Across a Pattern Layer in Photolithography Processes

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Throughput and Quality Optimized Down-Selection of Overlay Measurement Markers for Robust Control of the Maximum Overlay Error in a Pattern Layer in Photolithography Processes

  IEEE Transactions on Semiconductor Manufacturing · 2025-02-18 · 3 citations

  articleSenior author

  This paper presents a metaheuristic optimization-based approach for selecting a pre-determined number of measurement markers from the set of available markers that optimizes the performance of the recently introduced robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm { L}}^{\infty }$ </tex-math></inline-formula> norm overlay control algorithm, which robustly minimizes the worst overlay error across a given pattern layer. This optimization is then used in a Design of Experiments (DOE) setting to build a tractable regression model of a customizable objective function encompassing cost effects of quality losses and throughput benefits resulting from the down-selection of markers selected for robust overlay control. Using this model, one can rapidly determine the optimal proportion of markers for any set of cost parameters, and the optimal subset forming this proportion of available markers can be down-selected to maximize performance of the resulting robust overlay controller. Overlay data and models from a semiconductor manufacturing fab were used to evaluate the newly proposed inspection and control strategy. Results clearly indicate that the novel strategic down-selection of measurement markers coupled with robust overlay control could lead to vastly improved throughputs without decreasing quality relative to what can be achieved using traditional Run-to-Run (R2R) control. Feasibility of the novel DOE-based optimization was demonstrated for two scenarios of cost-effect parameters.

  PublisherDOI

• Robust Multilayer Control of Critical Overlay Error Components Across a Pattern Layer in Photolithography Processes

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Development and application of artificial intelligence in manufacturing systems – one approach

  Procedia CIRP · 2025-01-01

  articleOpen access

  Artificial intelligence (AI) that is applied in the entire chain of the new value creation and product life cycle has become the most important part of the Industry 4.0/5.0 model. The history of AI is a little more than eight decades long, and in research and development for manufacturing, it has been applied since the mid-1980s. Expert systems (ES) were the first AI tools implemented in this field. The aim of this paper is to perform a systemic analysis of the state of development and application of AI in manufacturing, which is originally used as support to the engineer, planner, designer and manager of various mechanical products. It is also used to manage processes and systems in manufacturing engineering. The paper is structured in such way that it provides answers to the following questions: how did AI models in manufacturing systems come about and develop, what are today’s models and the perspectives of applying AI in them, and some directions of future research in this area. As a special point of this paper, a review of some of our research results in this area obtained in the last few decades are presented.

  PublisherDOI

• System-level evaluation of productivity and quality in semiconductor frontend fabrication integrating product and process models

  CIRP journal of manufacturing science and technology · 2025-05-31 · 1 citations

  articleOpen access

  In semiconductor manufacturing, photolithography represents the core process of frontend fabrication as the quality outcome in terms of overlay errors depends entirely on it. Hence, particular attention is devoted to the inspection of each wafer layer, having 100 % measurements of markers distributed across a wafer with subsequent long inspection times. At the same time, process control is based on each layer's overall measurements, discouraging companies from improving productivity by reducing inspection time. As a consequence, in this context, the product, process and system are extremely inter-related. Recent developments in joint product-process modelling show that robust model-based control coupled with optimal down-selection of measurement markers enables improved process control without decreasing the quality. However, when considering the system level effects, new dynamics should be accounted for in order to make decisions about production system configuration and operations. This paper proposes a novel analytical model for the evaluation of quality and productivity performance in manufacturing systems characterized by propagation of quality errors, process adaptation and alternative inspection policies. The proposed model is general, but particularly useful for the semiconductor sector. Application of this method to an industrial-scale semiconductor manufacturing system shows that when product-process-system are considered together, global optimal solutions can be achieved.

  PublisherDOI

Recent grants

• GOALI: Immune System Engineering for Automotive Engine Systems

  NSF · $63k · 2007–2008

• I-UCRC: The University of Texas at Austin Site of the I-UCRC on Intelligent Maintenance Systems

  NSF · $336k · 2013–2018

Frequent coauthors

• Jun Ni

  26 shared

• Ramin Sabbagh

  The University of Texas at Austin

  20 shared

• Vidosav Majstorović

  14 shared

• Michael E. Cholette

  Queensland University of Technology

  14 shared

• Jay Lee

  13 shared

• S. V. Sreenivasan

  12 shared

• Alec Stothert

  10 shared

• Merve Çelen

  The University of Texas at Austin

  9 shared

Education

• B.S., Mechanical Eng. and Applied Mathematics

  Univ. of Nis, Serbia

  1997

• M.S., Mechanical Eng.

  Nanyang Technological Univ., Singapore

  1999

• M.S., Electrical Eng. (Systems)

  Univ. of Michigan, Ann Arbor

• Ph.D., Mechanical Eng.

  Univ. of Michigan, Ann Arbor

  2002

Awards & honors

• 2018 August-Wilhelm Scheer Visiting Professorship from Techn…
• 2006 Outstanding Young Manufacturing Engineer Award from the…
• 2005 Teaching Incentive Award from the Department of Mechani…