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Abstract:

Piezoelectric materials are integral to ultrasound probes and scanning devices in medical imaging and fingerprint recognition, as they can convert mechanical energy into electrical energy. This conversion enables the imaging of internal structures, facilitating medical diagnostics by highlighting deviations from normal organ dimensions. Traditionally, Lead Zirconate Titanate (PZT-4) has been used in handheld ultrasound probes, despite its low output and significant environmental hazards upon disposal. This paper presents Aluminium Nitride (AlN) as a safer, environmentally friendly, and thermally stable alternative. AlN is compatible with Complementary Metal Oxide Semiconductor (CMOS) technology, making it a viable option for sophisticated ultrasound probes that can be compact enough to be taken into the body. The simulations conducted through COMSOL Multiphysics at 200 kHz, this study demonstrate AlN's piezo acoustic properties, which are crucial for generating photoacoustic images in biomedical imaging. The presented simulation model enables monitoring of the material's acoustic behavior in response to specific electrical inputs and frequencies.

Keywords:

Acoustic,Aluminium nitride,Piezoelectric,COMSOL Multiphysics,Frequency,Ultrasound,

Refference:

I. A. Abu-libdeh, and A. Emadi, “Piezoelectric Micromachined Ultrasonic Transducers (PMUTs): Performance Metrics, Advancements, and Applications,” Sensors, Vol: 22, no: 23, Nov. 2022, 10.3390/s22239151.

II. A. Guedes, et al., “Aluminum nitride pMUT based on a flexurally-suspended membrane,” 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference, Beijing, China, 2011, pp: 2062-2065. 10.1109/TRANSDUCERS.2011.5969223.
III. A. Iula, “Ultrasound systems for biometric recognition,” Sensors Review, Vol: 19, no. 10, May 2019. 10.3390/s19102317
IV. A. Neprokin, C. Broadway, T. Myllylä, A. Bykov, and I. Meglinski, “Photoacoustic imaging in biomedicine and life sciences,” Life, Vol: 12, no: 4, p. 588, Apr. 2022. 10.3390/life12040588.
V. A. Safari, Q. Zhou, Y. Zeng, and J. D. Leber, “Advances in development of Pb-free piezoelectric materials for transducer applications,”Japanese Journal of Applied Physics, Vol: 62, no: SJ, p: SJ0801, Mar. 2023, 10.35848/1347-4065/acc812.
VI. Akasheh, et al., “Development of Piezoelectric Micromachined UltrasonicTransducers,” Sensors Actuators Applied Physics. Vol: 111, pp: 275– 287, Mar. 2024, 10.1016/j.sna.2003.11.022
VII. B. Herrera, P. Simeoni, G. Giribaldi, L. Colombo, and M. Rinaldi, “Scandium-Doped Aluminum Nitride PMUT Arrays for Wireless Ultrasonic Powering of Implantables,” IEEE Open Journal of Ultrasonics Ferroelectrics and Frequency Control, Vol: 2, pp: 250–260, Jan. 2022, 10.1109/ojuffc.2022.3221708.
VIII. C. Cheng, “Piezoelectric Micromachined Ultrasound Transducers Using Lead Zirconate Titanate Films,” Ph.D. dissertation, Dept. Materials Science and Engg., Pennsylvania State Univ., Pennsylvania, USA, 2021.
IX. C. Fei et al., “Ultrahigh frequency (100 MHz–300 MHz) ultrasonic transducers for optical resolution medical imagining,” Scientific Reports, Vol: 6, no: 1, Jun. 2016. 10.1038/srep28360.
X. D. O. Urroz-Montoya, J. R. Alverto-Suazo, J. R. Garcfa-Cabrera, and C. H. Ortega-Jimenez, “Piezoelectricity: a literature review for power generation support,”MATEC Web of Conferences, Vol: 293, p: 05004, Jan. 2019, 10.1051/matecconf/201929305004.
XI. E. G. Nesvijski, “Some aspects of ultrasonic testing of composites,” Composite Structures, Vol: 48, pp:151–155, Mar 2000, doi: 10.1016/S0263-8223(99)00088-4
XII. E. Ledesma, I. Zamora, A. Uranga, F. Torres, and N. Barniol, “Enhancing AlN PMUTs’ Acoustic Responsivity within a MEMS-on-CMOS Process,”Sensors, Vol: 21, no. 24, p. 8447, Dec. 2021. 10.3390/s21248447.

XIII. F. Pop, B. Herrera, and M. Rinaldi, “Lithium Niobate Piezoelectric Micromachined Ultrasonic Transducers for high data-rate intrabody communication,” Nature Communications, Vol: 13, no: 1, Apr. 2022. 10.1038/s41467-022-29355-9.
XIV. F. Pop, B. Herrera, C. Cassella, and M. Rinaldi, “Modeling and optimization of directly modulated piezoelectric micromachined ultrasonic transducers,” Sensors, Vol: 21, no: 1, p: 157, Dec. 2020. 10.3390/s21010157.
XV. H. Wang, “Development of Piezoelectric Micromachined Ultrasonic Transducers (Pmuts) For Endoscopic Photoacoustic Imaging Applications,” Ph.D. dissertation, Dept. Electri. and Comp. Engg., University Of Florida, Gainesville, Florid, USA, 2021.
XVI. H. Wang, Z. Chen, H. Yang, H. Jiang, and H. Xie, “A ceramic PZT-Based PMUT array for endoscopic photoacoustic imaging,” Journal of Microelectromechanical Systems, Vol: 29, no. 5, pp: 1038–1043, Jul. 2020, 10.1109/jmems.2020.3010773.
XVII. J. Cai et al., “Photoacoustic imaging based on broadened bandwidth aluminum nitride piezoelectric micromachined ultrasound transducers,” IEEE Sensors Letters, Vol: 7, no: 4, pp: 1–4, Mar. 2023. 10.1109/lsens.2023.3254593
XVIII. J. Jung, W. Lee, W. Kang, E. Shin, J. Ryu, and H. Choi, “Review of piezoelectric micromachined ultrasonic transducers and their applications,” Journal of Micromechanics and Microengineering, Vol: 27, no: 11, p; 113001, Aug. 2017. 10.1088/1361-6439/aa851b
XIX. J. Kim et al., “A Rapid Prototyping Method for Sub-MHz Single-Element Piezoelectric Transducers by Using 3D- Printed Components,” Sensors, Vol: 23, no: 1, Dec 2022. 10.3390/s23010313
XX. J. Pan, C. Bai, Q. Zheng, and H. Xie, “Review of Piezoelectric Micromachined Ultrasonic Transducers for Rangefinders,” Micromachines, Vol: 14, no: 2, p: 374, Feb. 2023. 10.3390/mi14020374.
XXI. J. Y. Pyun, Y. H. Kim and K. K. Park. “Design of Piezoelectric Acoustic Transducers for Underwater Applications,” Sensors, Vol: 23, no. 4, Feb. 2023. 10.3390/s23041821
XXII. J.E. Aldrich, “Basic Physics of Ultrasound Imaging,” Crit. Care Med., Vol: 35 pp: S131–S137, May 2007. 10.1097/01.ccm.0000260624.99430.22.
XXIII. M. Darmon, G. Toullelan and V. Dorval, “An Experimental and Theoretical Comparison of 3D Models for Ultrasonic Non-Destructive Testing of Cracks: Part I, Embedded Cracks,” Applied Sciences, Vol:12, no: 10, May 2022. 10.3390/app12105078
XXIV. M. Ishikawa et al., “Lead Zirconate Titanate Thick-Film Ultrasonic Transducer for 1 to 20 MHz Frequency Bands Fabricated by Hydrothermal Polycrystal Growth,” Japanese Journal of Applied Physics, Vol: 44, no: 6S, pp:4342, Jun. 2005. 10.1143/JJAP.44.4342
XXV. M. Lethiecq et al., Piezoelectric Transducer Design for Medical Diagnosis and NDE”, Piezoelectric and Acoustic Materials for Transducer Applications, Springer US, pp:191-215, 2008. 10.1007/978-0-387-76540-2_10
XXVI. Q. Yu et al., “PZT-Film-Based Piezoelectric Micromachined Ultrasonic Transducer with I-Shaped Composite Diaphragm,” Micromachines, Vol: 13, no: 10, p: 1597, Sep. 2022. 10.3390/mi13101597.
XXVII. Q. Zhou et al., “Piezoelectric Single Crystal Ultrasonic Transducers for Biomedical Applications,” Prog. Mater. Sci. Vol:66, pp: 87–111, Oct. 2014, 10.1016/j.pmatsci.2014.06.001
XXVIII. R. Manwar, K. Kratkiewicz, and K. Avanaki, “Overview of Ultrasound Detection Technologies for Photoacoustic Imaging,” Micromachines, Vol: 11, no: 7, p: 692, Jul. 2020. 10.3390/mi11070692.
XXIX. W. Lee, and Y. Roh, “Ultrasonic transducers for medical diagnostic imaging,” Biomedical Engineering Letters, Vol: 7, pp: 91–97, Mar. 2017. 10.1007/s13534-017-0021-8
XXX. Xin Su et al., “Simulation and Optimization of Piezoelectric Micromachined Ultrasonic Transducer Unit Based on AlN,” Electronics, Vol:11, no: 18, 2022. 10.3390/electronics11182915
XXXI. Y. Li, T. Omori, K. Watabe and H. Toshiyoshi, “Bandwidth and Sensitivity Enhancement of Piezoelectric MEMS Acoustic Emission Sensor Using Multi-Cantilevers,”2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Tokyo, Japan, 2022, pp: 868-871. 10.1109/MEMS51670.2022.9699581.

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Abstract:

This paper presents a novel consensus clustering framework that integrates both cluster-level and clustering-level weighting strategies. Traditional consensus clustering methods either weight the clusters or the base clusterings, but often fail to optimally combine these two strategies. We propose a dual-weighting scheme where weights are assigned to clusters based on internal and external consistency, and to the base clusterings based on their agreement with the ensemble. By applying a combined weight, we ensure that both high-quality clusters and consistent clusterings contribute more to the final consensus. Experimental results on several benchmark datasets demonstrate the superiority of the proposed method over existing clustering ensemble techniques.

Keywords:

Consensus Clustering,Clustering Ensemble,Clustering Techniques,Dual-Weighted Approach,

Refference:

I. Ali, N., M. S. Uddin, J. M. Kim: “A Cluster Validity Index-Based Weighted Clustering Ensemble Framework.” Information Sciences, vol. 478, 2019, pp. 238-55. 10.1016/j.ins.2018.11.053.
II. Banerjee, A.: “Leveraging Frequency and Diversity Based Ensemble Selection to Consensus Clustering.” 2014 Seventh International Conference on Contemporary Computing (IC3), 2014, pp. 123-29.
III. Banerjee, A., B. Pati, C. Panigrahi: “SC2: A Selection Based Consensus Clustering Approach.” International Conference on Advanced Computing and Intelligent Engineering (ICACIE 2016), Springer, 2016.
IV. Banerjee, A., C. Panigrahi, B. Pati, S. Nayak: “Entropy-Based Cluster Selection.” Progress in Advanced Computing and Intelligent Engineering, Springer, 2021, pp. 313-21.
V. Banerjee, A., S. Nayak, C. Panigrahi, B. Pati: “Clustering Ensemble by Clustering Selected Weighted Clusters.” International Journal of Computational Science and Engineering, vol. 27, no. 2, 2024, pp. 159-66.
VI. Calinski, T., J. Harabasz: “A Dendrite Method for Cluster Analysis.” Communications in Statistics – Theory and Methods, vol. 3, no. 1, 1974, pp. 1-27. 10.1080/03610927408827101.

VII. Dunn, J. C.: “Well-Separated Clusters and Optimal Fuzzy Partitions.” Journal of Cybernetics, vol. 4, no. 1, 1974, pp. 95-104. 10.1080/01969727408546059.
VIII. Dua, D., C. Graff: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences, 2019. archive.ics.uci.edu/ml.
IX. Fred, A. L., A. K. Jain: “Finding Consistent Clusters in Data Partitions.” Proc. 3rd Int. Workshop on Multiple Classifier Systems, 2002, pp. 309-18.
X. Geddes, T.: “Autoencoder-Based Cluster Ensembles for Single-Cell RNA-seq Data Analysis.” BMC Bioinformatics, vol. 20, 2019, p. 660.
XI. Gu, Q., Y. Wang, P. Wang, X. Li, L. Chen, N. N. Xiong, D. Liu: “An Improved Weighted Ensemble Clustering Based on Two-Tier Uncertainty Measurement.” Expert Systems with Applications, vol. 238, 2024, art. 121672. 10.1016/j.eswa.2023.121672.
XII. Halkidi, M., Y. Batistakis, M. Vazirgiannis: “On Clustering Validation Techniques.” Journal of Intelligent Information Systems, vol. 17, no. 2, 2001, pp. 107-45.
XIII. Huang, D., C. Wang, J. Lai: “LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering.” Neural Information Processing: 24th International Conference, Springer, 2017, pp. 167-76.
XIV. Huang, D., C. Wang, J. Lai: “Locally Weighted Ensemble Clustering.” IEEE Transactions on Cybernetics, vol. 48, no. 5, 2018, pp. 1460-73.
XV. Huang, D., C. Wang, J. Wu, J. Lai, C. Kwoh: “Ultra-Scalable Spectral Clustering and Ensemble Clustering.” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, 2020, pp. 1212-26. 10.1109/TKDE.2019.2903410.
XVI. Jia, J., X. Xiao, B. Liu, L. Jiao: “Bagging-Based Spectral Clustering Ensemble Selection.” Pattern Recognition Letters, vol. 32, no. 10, 2011, pp. 1456-67.
XVII. Li, N., S. Xu, H. Xu: “A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble.” Neural Processing Letters, vol. 56, 2024, art. 183. 10.1007/s11063-024-11618-9.
XVIII. Nazari, A., M. Kargari, B. Mohammadzadeh Asl, R. Hosseini: “A Comprehensive Study of Clustering Ensemble Weighting Based on Cluster Quality and Diversity.” Pattern Analysis and Applications, vol. 22, no. 1, 2019, pp. 133-45.
XIX. Rand, W. M.: “Objective Criteria for the Evaluation of Clustering Methods.” Journal of the American Statistical Association, vol. 66, no. 336, 1971, pp. 846-50. 10.1080/01621459.1971.10482238.

XX. Rousseeuw, P. J.: “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics, vol. 20, 1987, pp. 53-65. 10.1016/0377-0427(87)90125-7.
XXI. XXI. Sedghi, M., E. Akbari, H. Motameni, T. Banirostam: “Clustering Ensemble Extraction: A Knowledge Reuse Framework.” Advances in Data Analysis and Classification, 2024. 10.1007/s11634-024-00588-4.
XXII. Shan, Y., S. Li, F. Li, Y. Cui, M. Chen: “Dual-Level Clustering Ensemble Algorithm with Three Consensus Strategies.” Scientific Reports, vol. 13, 2023. 10.1038/s41598-023-49947-9.
XXIII. Strehl, A., J. Ghosh: “Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions.” Journal of Machine Learning Research, vol. 3, 2002, pp. 583-617.
XXIV. Tatarnikov, V. V., I. A. Pestunov, V. B. Berikov: “Centroid Averaging Algorithm for a Clustering Ensemble.” Computer Optics, vol. 41, no. 5, 2017, pp. 712-18. 10.18287/2412-6179-2017-41-5-712-718.
XXV. Vega-Pons, S., E. Correa-Morris, J. Ruiz-Shulcloper: “Weighted Partition Consensus via Kernels.” Pattern Recognition, vol. 41, no. 6, 2008, pp. 1943-53. 10.1016/j.patcog.2007.11.018.
XXVI. Vinh, N. X., J. Epps, J. Bailey: “Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance.” Journal of Machine Learning Research, vol. 11, 2010, pp. 2837-54.
XXVII. Yang, H., S. Wang, C. Zhou: “A Novel Clustering Ensemble Method Based on Community Detection in Complex Networks.” Neurocomputing, vol. 214, 2016, pp. 598-605. 10.1016/j.neucom.2016.06.012.
XXVIII. Zhang, M.: “Weighted Clustering Ensemble: A Review.” Pattern Recognition, vol. 124, 2022, art. 108428. 10.1016/j.patcog.2021.108428.
XXIX. Zhou, Z., W. Tang: “Clusterer Ensemble.” Knowledge-Based Systems, vol. 19, no. 1, 2006, pp. 77-83.

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