Modeling of Quasi-Static Floating-Gate Transistor Biosensors

Mathew S. Thomas, Demetra Z. Adrahtas, C. Daniel Frisbie, Kevin D. Dorfman

Research output: Contribution to journalArticlepeer-review

Abstract

Floating-gate transistors (FGTs) are a promising class of electronic sensing architectures that separate the transduction elements from molecular sensing components, but the factors leading to optimum device design are unknown. We developed a model, generalizable to many different semiconductor/dielectric materials and channel dimensions, to predict the sensor response to changes in capacitance and/or charge at the sensing surface upon target binding or other changes in surface chemistry. The model predictions were compared to experimental data obtained using a floating-gate (extended gate) electrochemical transistor, a variant of the generic FGT architecture that facilitates low-voltage operation and rapid, simple fabrication using printing. Self-assembled monolayer (SAM) chemistry and quasi-statically measured resistor-loaded inverters were utilized to obtain experimentally either the capacitance signals (with alkylthiol SAMs) or charge signals (with acid-terminated SAMs) of the FGT. Experiments reveal that the model captures the inverter gain and charge signals over 3 orders of magnitude variation in the size of the sensing area and the capacitance signals over 2 orders of magnitude but deviates from experiments at lower capacitances of the sensing surface (<1 nF). To guide future device design, model predictions for a large range of sensing area capacitances and characteristic voltages are provided, enabling the calculation of the optimum sensing area size for maximum charge and capacitance sensitivity.

Original languageEnglish (US)
Pages (from-to)1910-1917
Number of pages8
JournalACS Sensors
Volume6
Issue number5
DOIs
StatePublished - May 28 2021

Bibliographical note

Funding Information:
Portions of this work were conducted at the Minnesota Nano Center which is supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network (NNIN) under award numbers ECCS-1542202 and ECCS-2025124. This work was supported by the Michael H. Baker Foundation and IPRIME. D.Z.A. was supported by a Biotechnology Training (grant no. NIH T32GM008347).

Publisher Copyright:
© 2021 American Chemical Society.

Keywords

  • biosensing
  • electronic detection
  • extended gate
  • field-effect transistor
  • floating gate
  • modeling

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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