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Machine Learning in FET-based Chemical and Biological Sensors: A Mini Review

안재혁
한국센서학회|2021.01|센서학회지 (Journal of Sensor Science and Technology)|제30권 제1호|pp. 1-9

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키워드

Machine learningfield-effect transistorsbiosensorsgas sensorsand receptors.
주제분류공학일반

초록

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specificfocus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key MLtechniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysisbased on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-basedsensors are briefly considered.

Abstract

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specificfocus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key MLtechniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysisbased on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-basedsensors are briefly considered.

참고문헌 (48)

  1. M. Kaisti, "Detection principles of biological and chemical FET sensors", Biosens. Bioelectron, 98, pp.437~448 (2017)
  2. T. Lee, "Recent advances in AIV biosensors composed of nanobio hybrid material", Micromachines, 9(12), pp.651~ (2018)
  3. T. Lee, "Development of the Troponin Detection System Based on the Nanostructure", Micromachines, 10(3), pp.203~ (2019)
  4. M.-Z. Li, "Recent Advances in Flexible Field-Effect Transistors toward Wearable Sensors", Adv. Intell. Syst., 2(11), pp.2000113~ (2020)
  5. Y. LeCun, "Deep learning", Nature, 521(7553), pp.436~444 (2015)
  6. A. F. de Almeida, "Synthetic organic chemistry driven by artificial intelligence", Nat. Rev. Chem., 3(10), pp.589~604 (2019)
  7. Y. Gil, "Amplify scientific discovery with artificial intelligence", Science, 346(6206), pp.171~172 (2014)
  8. K. A. Brown, "Machine Learning in Nanoscience : Big Data at Small Scales", Nano Lett., 20(1), pp.2~10 (2019)
  9. Y. Cui, "Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species", Science, 293(5533), pp.1289~1292 (2001)
  10. E. Stern, "Label-free immunodetection with CMOScompatible semiconducting nanowires", Nature, 445(7127), pp.519~522 (2007)
  11. M. A. H. Khan, "Gallium Nitride(GaN)Nanostructures and Their Gas Sensing Properties : A Review", Sensors, 20(14), pp.3889~ (2020)
  12. Q. Liu, "Highly sensitive and quick detection of acute myocardial infarction biomarkers using In2O3 nanoribbon biosensors fabricated using shadow masks", ACS Nano, 10(11), pp.10117~10125 (2016)
  13. A. K. Wanekaya, "Field-effect transistors based on single nanowires of conducting polymers", J. Phys. Chem. C, 111(13), pp.5218~5221 (2007)
  14. J. Kwon, "Nanoscale FET-based transduction toward sensitive extended-gate biosensors", ACS Sens., 4(6), pp.1724~1729 (2019)
  15. J. -H. Ahn, "Palladium nanoparticle decorated silicon nanowire field-effect transistor with side-gates for hydrogen gas detection", Appl. Phys. Lett., 104(1), pp.013508~ (2014)
  16. Young Geun Song, "Design of Semiconducting Gas Sensors for Room-Temperature Operation", 센서학회지, 29(1), pp.1~6 (2020), 한국센서학회
  17. I. Lundström, "A hydrogen− sensitive MOS field− effect transistor", Appl. Phys. Lett., 26(2), pp.55~57 (1975)
  18. P. R. Nair, "Design considerations of silicon nanowire biosensors", IEEE Trans. Electron Devices, 54(12), pp.3400~3408 (2007)
  19. C. M. Bishop, "Pattern recognition and machine learning" (2006), Springer
  20. Z. Li, "The optoelectronic nose : colorimetric and fluorometric sensor arrays", Chem. Rev., 119(1), pp.231~292 (2018)
  21. D. A. Pisner, "Machine Learning", pp.101~121 (2020), Elsevier
  22. A. Krogh, "What are artificial neural networks?", Nat. Biotechnol., 26(2), pp.195~197 (2008)
  23. M. Scholz, "Approaches to analyse and interpret biological profile data", Ph.D., pp.2006
  24. Y. Jiang, "A chemiresistive sensor array from conductive polymer nanowires fabricated by nanoscale soft lithography", Nanoscale, 10(44), pp.20578~20586 (2018)
  25. S. -J. Kim, "Innovative nanosensor for disease diagnosis", Acc. Chem. Res., 50(7), pp.1587~1596 (2017)
  26. M. S. Wiederoder, "Graphene nanoplatelet-polymer chemiresistive sensor arrays for the detection and discrimination of chemical warfare agent simulants", ACS Sens., 2(11), pp.1669~1678 (2017)
  27. Y. Rong, "Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions", Analyst, 143(9), pp.2066~2075 (2018)
  28. L. A. Horsfall, "An array of WO3 and CTO heterojunction semiconducting metal oxide gas sensors used as a tool for explosive detection", J. Mater. Chem. A, 5(5), pp.2172~2179 (2017)
  29. L. Bian, "Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors", ACS Appl. Mater. Interfaces, 11(1), pp.1219~1227 (2018)
  30. B. Wang, "Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase", Nano Lett., 14(2), pp.933~938 (2014)
  31. M. K. Nakhleh, "Diagnosis and classification of 17diseases from 1404 subjects via pattern analysis of exhaled molecules", ACS Nano, 11(1), pp.112~125 (2017)
  32. B. Kim, "Carboxylated Single-Walled Carbon Nanotube Sensors with Varying pH for the Detection of Ammonia and Carbon Dioxide Using an Artificial Neural Network", ACS Appl. Nano Mater., 2(10), pp.6445~6451 (2019)
  33. J. -H. Ahn, "Understanding the signal amplification in dual-gate FET-based biosensors", J. Appl. Phys, 128(18), pp.184502~ (2020)
  34. B. Cao, "How to optimize materials and devices via design of experiments and machine learning : Demonstration using organic photovoltaics", ACS Nano, 12(8), pp.7434~7444 (2018)
  35. S. Manzeli, "2D transition metal dichalcogenides", Nat. Rev. Mater., 2(8), pp.17033~ (2017)
  36. Z. Lin, "2D materials advances : from large scale synthesis and controlled heterostructures to improved characterization techniques, defects and applications", 2D Mater, 3(4), pp.042001~ (2016)
  37. A. K. Geim, "Van der Waals heterostructures", Nature, 499(7459), pp.419~425 (2013)
  38. T. Hussain, "Defect and substitution-induced silicene sensor to probe toxic gases", J. Phys. Chem. C, 120(44), pp.25256~25262 (2016)
  39. N. C. Frey, "Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing", ACS Nano, 14(10), pp.13406~13417 (2020)
  40. C.-H. Lee, "Atomically thin p–n junctions with van der Waals heterointerfaces", Nat. Nanotechnol., 9(9), pp.676~ (2014)
  41. L. Bassman, "Active learning for accelerated design of layered materials", npj Comput. Mater, 4(1), pp.1~9 (2018)
  42. P. Lin, "Organic thin-film transistors for chemical and biological sensing", Adv. Mater., 24(1), pp.34~51 (2012)
  43. M. H. Lee, "Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n-Type Organic Field-Effect Transistors", Adv. Electron. Mater., 5(12), pp.1900573~ (2019)
  44. S. Mazurenko, "Machine learning in enzyme engineering", ACS Catal., 10(2), pp.1210~1223 (2019)
  45. J. Song, "A Sequential Multidimensional Analysis Algorithm for Aptamer Identification based on Structure Analysis and Machine Learning", Anal. Chem., 92(4), pp.3307~3314 (2019)
  46. N. Nakatsuka, "Aptamer–field-effect transistors overcome Debye length limitations for small-molecule sensing", Science, 362(6412), pp.319~324 (2018)
  47. Y. Kim, "A bioinspired flexible organic artificial afferent nerve", Science, 360(6392), pp.998~1003 (2018)
  48. C. Wan, "An artificial sensory neuron with tactile perceptual learning", Adv. Mater., 30(30), pp.1801291~ (2018)

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