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A Computational Modeling for Reconfigurable Biosensors.

Roberta Grasso¹, Jose M Gonzalez-Medina², Gian Luca Barbruni¹

¹Bio/CMOS Interfaces Laboratory, EPFL, Neuchatel, 2000 Switzerland.

|April 13, 2026

Summary

This summary is machine-generated.

This study introduces a novel multiscale modeling approach for Reconfigurable Field Effect Transistor (RFET) biosensors. This method links molecular binding to electrical signals, enabling dual-polarity detection for enhanced biosensing applications.

Keywords:

Aptamer Molecular dynamic simulation Reconfigurable transistor Sensors Silicon nanowire TCAD

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Area of Science:

Nanotechnology
Biosensors
Computational Modeling

Background:

Field-effect transistor (FET) biosensors detect biomolecules via surface potential changes.
Traditional FETs with fixed doping are limited to detecting either positive or negative targets.
Reconfigurable Field Effect Transistors (RFETs) offer dynamic n- and p-type switching, overcoming doping limitations.

Purpose of the Study:

To present a novel multiscale modeling approach for RFET biosensors.
To explore the potential of RFETs as dual-polarity, high-sensitivity biosensors.
To establish a link between molecular binding events and device electrical response.

Main Methods:

Combining molecular dynamics (MD) simulations for probe-target binding.
Integrating TCAD simulations for Reconfigurable Field Effect Transistor (RFET) electrical behavior.
Utilizing aptamer and enzyme recognition elements for distinct binding mechanisms.

Main Results:

Demonstrated RFET adaptability for detecting both negatively and positively charged analytes.
Established a direct correlation between molecular-scale binding and device-level electrical output.
Provided mechanistic insights into the biosensing process for RFETs.

Conclusions:

The multiscale modeling framework enables rational design of RFET-based biosensors.
RFETs are promising for high-sensitivity, dual-polarity biosensing applications.
The methodology is applicable to various charge-based FET biosensors for optimization and trend prediction.