Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
Source abstract
Abstract
The semiconductor manufacturing processes have been evolved to improve the yield rate. Here, we studied a sequential and comprehensive algorithm that could be used for fault detection and classification (FDC) of the semiconductor chips. A statistical process control (SPC) method is employed for inspecting whether sensors used in the semiconductor manufacturing process become stable or not. When the sensors are individually stable, the algorithm conducts the relational inspection to identify the relationship between two sensors. The key factor here is the coefficient of determination (R2). If R2 is calculated as more than 0.7, their relationship is analyzed through the regression analysis, while the algorithm conducts the clustering analysis to the sensor pair with R2 less than 0.7. This analysis also provided the capability to determine whether the newly generated data are defective or defect-free. Therefore, this study is not only applied to the semiconductor manufacturing process but can also be to the various research fields where the big data are treated.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Industrial Vision Systems and Defect Detection · Advanced Statistical Process Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Engineering, Sogang University, Seoul 04107, Korea
Department of Mechanical Engineering, Sogang University, Seoul 04107, Korea
Division of Chemical Industry, Yeungnam University, Daegu 42415, Korea
Mechatronics R&D Center, Samsung Electronics, Suwon 16677, Korea
References (45)
Show source-provided references
1. Circularly polarized luminescence from semiconductor quantum rods templated by self-assembled cellulose nanocrystals10.1039/C9TC05751J ↗
2. Gohardani, A.S. (2018, January 17–19). Advances in Aerospace Engineering through Innovation in Packaging Design for High-Reliability Design Applications. Proceedings of the 2018 AIAA SPACE and Astronautics Forum and Exposition, Orlando, FL, USA.10.2514/6.2018-5224 ↗
3. High-capacity dynamic indoor all-optical-wireless communication system backed up with millimeter-wave radio techniques10.1109/JLT.2018.2847759 ↗
4. High-performance printed electronics based on inorganic semiconducting nano to chip scale structures10.1186/s40580-020-00243-6 ↗
5. Label-free detection of γ-aminobutyric acid based on silicon nanowire biosensor10.1186/s40580-019-0184-3 ↗
6. Challenges in paper-based fluorogenic optical sensing with smartphones10.1186/s40580-018-0146-1 ↗
7. Modeling iontophoretic drug delivery in a microfluidic device10.1039/D0LC00602E ↗
8. A Fully Integrated Closed-Loop System Based on Mesoporous Microneedles-Iontophoresis for Diabetes Treatment10.1002/advs.202100827 ↗
9. Biological small-molecule assays using gradient-based microfluidics10.1016/j.bios.2021.113038 ↗
10. Hollauer, C. (2007). Modeling of Thermal Oxidation and Stress Effects. [Ph.D. Thesis, Technical University of Vienna].
11. Manufacturing intelligence to forecast and reduce semiconductor cycle time10.1007/s10845-011-0572-y ↗
12. Hsieh, L.Y., and Hsieh, T.-J. (2018). A throughput management system for semiconductor wafer fabrication facilities: Design, systems and implementation. Processes, 6.10.3390/pr6020016 ↗
13. Improved electrical performance of a sol–gel IGZO transistor with high-k Al2O3 gate dielectric achieved by post annealing10.1186/s40580-019-0194-1 ↗
14. Algorithm analysis of gas bubble generation in a microfluidic device10.1007/s13206-018-3203-2 ↗
15. Data mining for yield enhancement in semiconductor manufacturing and an empirical study10.1016/j.eswa.2006.04.014 ↗
16. A review of process fault detection and diagnosis: Part III: Process history based methods10.1016/S0098-1354(02)00162-X ↗
17. Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing10.1007/s10845-020-01591-0 ↗
18. A new framework of simultaneous-fault diagnosis using pairwise probabilistic multi-label classification for time-dependent patterns10.1109/TIE.2012.2202358 ↗
19. Fault detection based on statistical multivariate analysis and microarray visualization
20. Semiconductor manufacturing process control and monitoring: A fab-wide framework10.1016/j.jprocont.2005.06.002 ↗
21. Fault detection of plasma etchers using optical emission spectra10.1109/66.857948 ↗
22. A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system10.1109/TSM.2017.2788501 ↗
23. Piecewise regression model construction with sample efficient regression tree (SERT) and applications to semiconductor yield analysis10.1016/j.jprocont.2012.05.017 ↗
24. Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes10.1109/TSM.2014.2374339 ↗
25. Clustering the dominant defective patterns in semiconductor wafer maps10.1109/TSM.2017.2768323 ↗
26. Regression methods for virtual metrology of layer thickness in chemical vapor deposition10.1109/TMECH.2013.2273435 ↗
27. Prediction analysis and quality assessment of microwell array images10.1002/elps.201700460 ↗
28. AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition10.1109/TSM.2019.2925361 ↗
29. Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing10.1109/TSM.2019.2917521 ↗
30. Scikit-learn: Machine learning in Python
31. Calculating the (almost) exact control limits for a C-chart10.1080/08982110600719472 ↗
32. A statistical process control method for semiconductor manufacturing10.9746/jcmsi.2.246 ↗
33. Reducing wafer delay time by robot idle time regulation for single-arm cluster tools10.1109/TASE.2020.3014078 ↗
34. Conductivity-limiting bipolar thermal conductivity in semiconductors10.1038/srep10136 ↗
35. Jyegal, J. (2017). Thermal energy diffusion incorporating generalized Einstein relation for degenerate semiconductors. Appl. Sci., 7.10.3390/app7080773 ↗
36. Berndt, D.J., and Clifford, J. (August, January 31). Using dynamic time warping to find patterns in time series. Proceedings of the KDD Workshop, Seattle, WA, USA.
37. Henein, M.M., Shawky, D.M., and Abd-El-Hafiz, S.K. (2018, January 26–28). Clustering-based Under-sampling for Software Defect Prediction. Proceedings of the ICSOFT, Porto, Portugal.10.5220/0006911402190227 ↗
38. Cluster-based minority over-sampling for imbalanced datasets10.1587/transinf.2016EDP7130 ↗
39. Küng, J., and Wagner, R. (2009). Transactions on Large-Scale Data-and Knowledge-Centered Systems I., Springer.
40. Sharing Machine Learning Models as Indicators of Compromise for Cyber Threat Intelligence10.3390/jcp1010008 ↗
41. Lee, J.-I., Kim, D.-H., Yoo, H.-J., Choi, H.-G., and Lee, Y.-S. (2021). Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression, Gradient Boosting, and CNN Algorithms. Diagnostics, 11.10.3390/diagnostics11071225 ↗
42. A novel method based on deep convolutional neural networks for wafer semiconductor surface defect inspection10.1109/TIM.2020.3007292 ↗
43. Deep learning for classification of the chemical composition of particle defects on semiconductor wafers10.1109/TSM.2019.2963656 ↗
44. Greco, A., Costantino, D., Morabito, F., and Versaci, M. (2003, January 20–24). A Morlet wavelet classification technique for ICA filtered SEMG experimental data. Proceedings of the International Joint Conference on Neural Networks, Portland, OR, USA.
45. Spectral–spatial classification of hyperspectral images using ICA and edge-preserving filter via an ensemble strategy10.1109/TGRS.2016.2553842 ↗