Deep-learning-based semantic image segmentation of graphene field-effect transistors

Ushiba, Shota; Miyakawa, Naruto; Ito, Naoya; Shinagawa, Ayumi; Nakano, Tomomi; Okino, Takashi; Sato, Hiroki; Oka, Yuka; Nishio, Madoka; Ono, Tetsuya; Kanai, Yasushi; Innami, Seiji; Tani, Shinsuke; Kimuara, Masahiko; Matstumoto, Kazuhiko

doi:10.35848/1882-0786/abe3db

Cited by 13 publications

(13 citation statements)

References 28 publications

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“…Recently, label-free methods using field-effect transistors (FETs) [8], surface plasmon resonance [9], and quartz crystal microbalance [10] have been used to detect CRP. In particular, great progress has been made in designing and fabricating FETs using nanomaterials, including Si nanowires [8,11,12], carbon nanotubes [13], and graphene [14][15][16], leading to a sensitive and rapid analysis with miniaturized and integrated sensor platforms [17,18].…”

Section: ◀ Significance ▶mentioning

confidence: 99%

Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

et al. 2022

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Section: ◀ Significance ▶mentioning

confidence: 99%

Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

et al. 2022

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“…These variations in the transfer curves may be attributed to the graphene tearing, PMMA residue on the graphene, and the impurity charge from the substrate. We previously reported that graphene tearing increases the resistance and decreases the transconductance [22]. It was also reported that both the PMMA residue and SiO 2 substrate have a p-doping effect on graphene [23][24][25].…”

Section: Transfer Curve Variability and Drifts Of Transfer Curvesmentioning

confidence: 95%

Drift Suppression of Solution-Gated Graphene Field-Effect Transistors by Cation Doping for Sensing Platforms

Miyakawa

Shinagawa

Kajiwara

et al. 2021

Sensors

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Solution-gated graphene field-effect transistors (SG-GFETs) provide an ideal platform for sensing biomolecules owing to their high electron/hole mobilities and 2D nature. However, the transfer curve often drifts in an electrolyte solution during measurements, making it difficult to accurately estimate the analyte concentration. One possible reason for this drift is that p-doping of GFETs is gradually countered by cations in the solution, because the cations can permeate into the polymer residue and/or between graphene and SiO2 substrates. Therefore, we propose doping sufficient cations to counter p-doping of GFETs prior to the measurements. For the pre-treatment, GFETs were immersed in a 15 mM sodium chloride aqueous solution for 25 h. The pretreated GFETs showed that the charge neutrality point (CNP) drifted by less than 3 mV during 1 h of measurement in a phosphate buffer, while the non-treated GFETs showed that the CNP was severely drifted by approximately 50 mV, demonstrating a 96% reduction of the drift by the pre-treatment. X-ray photoelectron spectroscopy analysis revealed the accumulation of sodium ions in the GFETs through pre-treatment. Our method is useful for suppressing drift, thus allowing accurate estimation of the target analyte concentration.

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“…As highly sensitive sensor devices, transistor-based biochemical sensors have great potential when combined with artificial intelligence (AI). Using machine learning (ML) methods to design devices and quantitatively analyze biological signals measured by transistor-based biochemical sensors may greatly improve detection accuracy and efficiency [ 20 , 21 , 22 , 23 , 24 ]. These developments will help transistor-based biochemical sensors pave the way for the next generation of point-of-care testing [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Applications of Transistor-Based Biochemical Sensors

Gao

Ming

2023

Biosensors

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Transistor-based biochemical sensors feature easy integration with electronic circuits and non-invasive real-time detection. They have been widely used in intelligent wearable devices, electronic skins, and biological analyses and have shown broad application prospects in intelligent medical detection. Field-effect transistor (FET) sensors have high sensitivity, reasonable specificity, rapid response, and portability and provide unique signal amplification during biochemical detection. Organic field-effect transistor (OFET) sensors are lightweight, flexible, foldable, and biocompatible with wearable devices. Organic electrochemical transistor (OECT) sensors convert biological signals in body fluids into electrical signals for artificial intelligence analysis. In addition to biochemical markers in body fluids, electrophysiology indicators such as electrocardiogram (ECG) signals and body temperature can also cause changes in the current or voltage of transistor-based biochemical sensors. When modified with sensitive substances, sensors can detect specific analytes, improve sensitivity, broaden the detection range, and reduce the limit of detection (LoD). In this review, we introduce three kinds of transistor-based biochemical sensors: FET, OFET, and OECT. We also discuss the fabrication processes for transistor sources, drains, and gates. Furthermore, we demonstrated three sensor types for body fluid biomarkers, electrophysiology signals, and development trends. Transistor-based biochemical sensors exhibit excellent potential in multi-mode intelligent analysis and are good candidates for the next generation of intelligent point-of-care testing (iPOCT).

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Deep-learning-based semantic image segmentation of graphene field-effect transistors

Cited by 13 publications

References 28 publications

Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

Drift Suppression of Solution-Gated Graphene Field-Effect Transistors by Cation Doping for Sensing Platforms

Applications of Transistor-Based Biochemical Sensors

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