Browse > Article
http://dx.doi.org/10.46670/JSST.2021.30.1.1

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

Ahn, Jae-Hyuk (Department of Electronics Engineering, Chungnam National University)
Publication Information
Journal of Sensor Science and Technology / v.30, no.1, 2021 , pp. 1-9 More about this Journal
Abstract
This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.
Keywords
Machine learning; field-effect transistors; gas sensors; biosensors; and receptors;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. R. Nair and M. A. Alam, "Design considerations of silicon nanowire biosensors", IEEE Trans. Electron Devices, Vol. 54, No. 12, pp. 3400-3408, 2007.   DOI
2 C. M. Bishop, Pattern recognition and machine learning, Springer, 2006.
3 Z. Li, J. R. Askim, and K. S. Suslick, "The optoelectronic nose: colorimetric and fluorometric sensor arrays", Chem. Rev., Vol. 119, No. 1, pp. 231-292, 2018.   DOI
4 D. A. Pisner and D. M. Schnyer, "Support vector machine" ,in Machine Learning: Elsevier, 2020, pp. 101-121.
5 A. Krogh, "What are artificial neural networks?", Nat. Biotechnol., Vol. 26, No. 2, pp. 195-197, 2008.   DOI
6 M. Scholz, Approaches to analyse and interpret biological profile data, University of Potsdam, Germany, Ph.D. thesis, 2006.
7 Y. Jiang, N. Tang, C. Zhou, Z. Han, H. Qu, and X. Duan, "A chemiresistive sensor array from conductive polymer nanowires fabricated by nanoscale soft lithography", Nanoscale, Vol. 10, No. 44, pp. 20578-20586, 2018.   DOI
8 S.-J. Kim, S.-J. Choi, J.-S. Jang, H.-J. Cho, and I.-D. Kim, "Innovative nanosensor for disease diagnosis", Acc. Chem. Res., Vol. 50, No. 7, pp. 1587-1596, 2017.   DOI
9 M. S. Wiederoder et al., "Graphene nanoplatelet-polymer chemiresistive sensor arrays for the detection and discrimination of chemical warfare agent simulants", ACS Sens., Vol. 2, No. 11, pp. 1669-1678, 2017.   DOI
10 Y. Rong et al., "Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions", Analyst, Vol. 143, No. 9, pp. 2066-2075, 2018.   DOI
11 L. A. Horsfall, D. C. Pugh, C. S. Blackman, and I. P. Parkin, "An array of WO3 and CTO heterojunction semiconducting metal oxide gas sensors used as a tool for explosive detection", J. Mater. Chem. A, Vol. 5, No. 5, pp. 2172-2179, 2017.   DOI
12 L. Bian et al., "Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors", ACS Appl. Mater. Interfaces, Vol. 11, No. 1, pp. 1219-1227, 2018.
13 B. Wang, J. C. Cancilla, J. S. Torrecilla, and H. Haick, "Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase", Nano Lett., Vol. 14, No. 2, pp. 933-938, 2014.   DOI
14 M. K. Nakhleh et al., "Diagnosis and classification of 17 diseases from 1404 subjects via pattern analysis of exhaled molecules", ACS Nano, Vol. 11, No. 1, pp. 112-125, 2017.   DOI
15 B. Kim, T. J. Norman, R. S. Jones, D.-I. Moon, J.-W. Han, and M. Meyyappan, "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., Vol. 2, No. 10, pp. 6445-6451, 2019.   DOI
16 M. Kaisti, "Detection principles of biological and chemical FET sensors", Biosens. Bioelectron., Vol. 98, pp. 437-448, 2017.   DOI
17 T. Lee et al., "Recent advances in AIV biosensors composed of nanobio hybrid material", Micromachines, Vol. 9, No. 12, pp. 651, 2018.   DOI
18 Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning", Nature, Vol. 521, No. 7553, pp. 436-444, 2015.   DOI
19 T. Lee et al., "Development of the Troponin Detection System Based on the Nanostructure", Micromachines, Vol. 10, No. 3, pp. 203, 2019.   DOI
20 M.-Z. Li, S.-T. Han, and Y. Zhou, "Recent Advances in Flexible Field-Effect Transistors toward Wearable Sensors", Adv. Intell. Syst., Vol. 2, No. 11, pp. 2000113, 2020.   DOI
21 A. F. de Almeida, R. Moreira, and T. Rodrigues, "Synthetic organic chemistry driven by artificial intelligence", Nat. Rev. Chem., Vol. 3, No. 10, pp. 589-604, 2019.   DOI
22 Y. Gil, M. Greaves, J. Hendler, and H. Hirsh, "Amplify scientific discovery with artificial intelligence", Science, Vol. 346, No. 6206, pp. 171-172, 2014.   DOI
23 K. A. Brown, S. Brittman, N. Maccaferri, D. Jariwala, and U. Celano, "Machine Learning in Nanoscience: Big Data at Small Scales", Nano Lett., Vol. 20, No. 1, pp. 2-10, 2019.   DOI
24 Y. Cui, Q. Wei, H. Park, and C. M. Lieber, "Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species", Science, Vol. 293, No. 5533, pp. 1289-1292, 2001.   DOI
25 E. Stern et al., "Label-free immunodetection with CMOS-compatible semiconducting nanowires", Nature, Vol. 445, No. 7127, pp. 519-522, 2007.   DOI
26 M. A. H. Khan and M. V. Rao, "Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review", Sensors, Vol. 20, No. 14, pp. 3889, 2020.   DOI
27 Q. Liu et al., "Highly sensitive and quick detection of acute myocardial infarction biomarkers using In2O3 nanoribbon biosensors fabricated using shadow masks", ACS Nano, Vol. 10, No. 11, pp. 10117-10125, 2016.   DOI
28 Y. G. Song, G. S. Kim, B.-K. Ju, and C.-Y. Kang, "Design of Semiconducting Gas Sensors for Room-Temperature Operation", J. Sens. Sci. Technol., Vol. 29, No. 1, pp. 1-6, 2020.   DOI
29 A. K. Wanekaya, M. A. Bangar, M. Yun, W. Chen, N. V. Myung, and A. Mulchandani, "Field-effect transistors based on single nanowires of conducting polymers", J. Phys. Chem. C, Vol. 111, No. 13, pp. 5218-5221, 2007.   DOI
30 J.-H. Ahn, J. Yun, Y.-K. Choi, and I. Park, "Palladium nanoparticle decorated silicon nanowire field-effect transistor with side-gates for hydrogen gas detection", Appl. Phys. Lett., Vol. 104, No. 1, pp. 013508, 2014.   DOI
31 I. Lundstrom, S. Shivaraman, C. Svensson, and L. Lundkvist, "A hydrogen- sensitive MOS field- effect transistor", Appl. Phys. Lett., Vol. 26, No. 2, pp. 55-57, 1975.   DOI
32 J.-H. Ahn, B. Choi, and S.-J. Choi, "Understanding the signal amplification in dual-gate FET-based biosensors", J. Appl. Phys, Vol. 128, No. 18, pp. 184502, 2020.   DOI
33 B. Cao et al., "How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics", ACS Nano, Vol. 12, No. 8, pp. 7434-7444, 2018.   DOI
34 S. Manzeli, D. Ovchinnikov, D. Pasquier, O. V. Yazyev, and A. Kis, "2D transition metal dichalcogenides", Nat. Rev. Mater., Vol. 2, No. 8, pp. 17033, 2017.   DOI
35 Z. Lin et al., "2D materials advances: from large scale synthesis and controlled heterostructures to improved characterization techniques, defects and applications", 2D Mater., Vol. 3, No. 4, pp. 042001, 2016.   DOI
36 J. Kwon et al., "Nanoscale FET-based transduction toward sensitive extended-gate biosensors", ACS Sens., Vol. 4, No. 6, pp. 1724-1729, 2019.   DOI
37 A. K. Geim and I. V. Grigorieva, "Van der Waals heterostructures", Nature, Vol. 499, No. 7459, pp. 419-425, 2013.   DOI
38 T. Hussain, T. Kaewmaraya, S. Chakraborty, and R. Ahuja, "Defect and substitution-induced silicene sensor to probe toxic gases", J. Phys. Chem. C, Vol. 120, No. 44, pp. 25256-25262, 2016.   DOI
39 N. C. Frey, D. Akinwande, D. Jariwala, and V. B. Shenoy, "Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing", ACS Nano, Vol. 14, No. 10, pp. 13406-13417, 2020.   DOI
40 C.-H. Lee et al., "Atomically thin p-n junctions with van der Waals heterointerfaces", Nat. Nanotechnol., Vol. 9, No. 9, pp. 676, 2014.   DOI
41 L. Bassman et al., "Active learning for accelerated design of layered materials", npj Comput. Mater., Vol. 4, No. 1, pp. 1-9, 2018.   DOI
42 P. Lin and F. Yan, "Organic thin-film transistors for chemical and biological sensing", Adv. Mater., Vol. 24, No. 1, pp. 34-51, 2012.   DOI
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., Vol. 5, No. 12, pp. 1900573, 2019.   DOI
44 S. Mazurenko, Z. Prokop, and J. Damborsky, "Machine learning in enzyme engineering", ACS Catal., Vol. 10, No. 2, pp. 1210-1223, 2019.
45 J. Song et al., "A Sequential Multidimensional Analysis Algorithm for Aptamer Identification based on Structure Analysis and Machine Learning", Anal. Chem., Vol. 92, No. 4, pp. 3307-3314, 2019.   DOI
46 N. Nakatsuka et al., "Aptamer-field-effect transistors overcome Debye length limitations for small-molecule sensing", Science, Vol. 362, No. 6412, pp. 319-324, 2018.   DOI
47 Y. Kim et al., "A bioinspired flexible organic artificial afferent nerve", Science, Vol. 360, No. 6392, pp. 998-1003, 2018.   DOI
48 C. Wan et al., "An artificial sensory neuron with tactile perceptual learning", Adv. Mater., Vol. 30, No. 30, pp. 1801291, 2018.   DOI