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1. Data-Efficient Prediction of Minimum Operating Voltage via Inter- and Intra-Wafer Variation Alignment

   Yin, Yuxuan; Chen, Rebecca; He, Chen; Li, Peng (April 2025, 2025 IEEE VLSI Test Symposium)

   Predicting the minimum operating voltage Vmin of chips stands as a crucial technique in enhancing the speed and reliability of manufacturing testing flow. However, existing Vmin prediction methods often overlook various sources of variations in both training and deployment phases. Notably, overlooking wafer zone-to-zone (intra-wafer) variations and wafer-to-wafer (inter-wafer) variations diminishes the accuracy, data efficiency, and reliability of Vmin predictors. To address this challenge, we propose Restricted Bias Alignment (RBA), a novel data-efficient Vmin prediction framework that introduces a variation alignment technique to simultaneously estimate inter- and intra-wafer variations. Furthermore, we propose utilizing class probe data to model inter-wafer variations for the first time. 

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2. RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines

   https://doi.org/10.3390/s24103244  

   Shyalika, Chathurangi; Roy, Kaushik; Prasad, Renjith; Kalach, Fadi El; Zi, Yuxin; Mittal, Priya; Narayanan, Vignesh; Harik, Ramy; Sheth, Amit (May 2024, Sensors)

   Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines. 

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3. Semi-Automated Soft Robotic Stamp Transfer Machine for Van Der Waals Heterostructure Device Assembly

   https://doi.org/10.1115/MSEC2023-101118  

   Ahn, Seong-Hyo; Chavarria, Juan; Mu, Haoxuan; Liao, Yifeng; Warner, Jamie; Zhou, Lei (June 2023, American Society of Mechanical Engineers)

   Monolayer materials can be vertically stacked into artificial solids, known as the van der Waals heterostructures (vd-WHs) [1], to realize a new class of ultrathin optoelectronic, electronic, and quantum devices, which have significant potential to revolutionize the field of nanoelectronics and impact a wide range of application areas including transparent displays, sensor arrays, and logic and memory circuits. However, today’s assembly of vdWH devices is still primarily through manual manipulation, which lacks the precision and repeatability needed for the scalable manufacturing of wafer-scale vdWH device arrays outside a research setting. Aiming to enable the automated, scalable, and repeatable manufacturing of vdWH device arrays, this paper presents the design, prototyping, and preliminary tests of a novel semi-automated soft-robotic stamp transfer system for thin-film materials. The system uses a dry elastomer stamp with its adhesion controlled by temperature and peeling speed for material transfer. A combination of electromagnetic and pneumatic actuation is used for the soft-robotic stamp to realize a gentle and uniform pressing of the stamp over the material. An optical microscope, force sensors, and temperature sensors are integrated to enable instrumentation of the transfer process. Preliminary experiments were conducted using our system to conduct for exfoliated graphite transfer. Test results demonstrate the reliable and repeatable transfer of 2D crystal flakes, which show promise to enable the deterministic and scalable assembly of vdWH-based device arrays at wafer scale. 

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4. Causal discovery in the presence of missing data

   Ruibo Tu, Cheng Zhang (April 2019, Proceedings of Machine Learning Research)

   Missing data are ubiquitous in many domain such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR),missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs),we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Miss-ing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections.Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR. 

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5. Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

   Ruibo Tu, Kun Zhang (December 2019, Advances in neural information processing systems)

   Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously difficult to be systematically evaluated because few datasets with known ground-truth causal relations are available. In this work, we handle the problem of evaluating causal discovery algorithms by building a flexible simulator in the medical setting. We develop a neuropathic pain diagnosis simulator, inspired by the fact that the biological processes of neuropathic pathophysiology are well studied with well-understood causal influences. Our simulator exploits the causal graph of theneuropathic pain pathology and its parameters in the generator are estimated from real-life patient cases. We show that the data generated from our simulator have similar statistics as real-world data. As a clear advantage, the simulator can produce infinite samples without jeopardizing the privacy of real-world patients. Our simulator provides a natural tool for evaluating various types of causal discovery algorithms, including those to deal with practical issues in causal discovery, such as unknown confounders, selection bias, and missing data. Using our simulator,we have evaluated extensively causal discovery algorithms under various settings. 

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