BackgroundIon Sensitive Field Effect Transistors (ISFETs) are one of the primitive structures for the fabrication of biosensors (BioFETs). Aiming at the optimization of the design and fabrication processes of BioFETs, the correlation between technological parameters and device electrical response can be obtained by means of an electrical device-level simulation. In this work we present a numerical simulation approach to the study of ISFET structures for bio-sensing devices (BioFET) using Synopsys Sentaurus Technology Computer-Aided Design (TCAD) tools.MethodsThe properties of a custom-defined material were modified in order to reproduce the electrolyte behavior. In particular, the parameters of an intrinsic semiconductor material have been set in order to reproduce an electrolyte solution.By replacing the electrolyte solution with an intrinsic semiconductor, the electrostatic solution of the electrolyte region can therefore be calculated by solving the semiconductor equation within this region.ResultsThe electrostatic behaviour (transfer characteristics) of a general BioFET structure has been simulated when the captured target number increases from 1 to 10. The ID current as a function of the VDS voltage for different positions of a single charged block and for different values of the reference electrode have been calculated.The electrical potential distribution along the electrolyte-insulator-semiconductor structure has been evaluated for different molar concentrations of the electrolyte solution.ConclusionsWe presented a numerical simulation approach to the study of Ion-Sensitive Field Effect Transistor (ISFET) structures for biosensing devices (BioFETs) using the Synopsys Sentaurus Technology Computer-Aided Design (TCAD) tools.A powerful framework for the design and optimization of biosensor has been devised, thus helping in reducing technology development time and cost. The main finding of the analysis of a general reference BioFET shows that there is no linear relationship between the number of charges and the current modulation. Actually, there is a strong position dependent effect: targets localized near the source region are most effective with respect to targets localized near the drain region. In general, even randomly distributed targets are more efficient with respect to locally grouped targets on the current modulation. Moreover, for the device at hand, a small positive biasing of the electrolyte solution, providing that the transistor goes on, will result in a greater enhancement of the current levels, still retaining a good sensitivity but greatly simplifying the operations of a real device.
[1]
Sergio Martinoia,et al.
Modeling H/sup +/-sensitive FETs with SPICE
,
1992
.
[2]
Andrei V. Bandura,et al.
The Ionization Constant of Water over Wide Ranges of Temperature and Density
,
2006
.
[3]
Gerhard Klimeck,et al.
Modeling and simulation of field-effect biosensors (BioFETs) and their deployment on the nanoHUB
,
2008
.
[4]
J. Rasaiah,et al.
Solvent Structure, Dynamics, and Ion Mobility in Aqueous Solutions at 25 °C
,
1998
.
[5]
Sung Min Seo,et al.
Simulation study on discrete charge effects of SiNW biosensors according to bound target position using a 3D TCAD simulator
,
2012,
Nanotechnology.
[6]
Clemens Heitzinger,et al.
Multi - Scale Modeling and Simulation of Field-Effect Biosensors
,
2008
.
[7]
P. Fromherz,et al.
A neuron-silicon junction: a Retzius cell of the leech on an insulated-gate field-effect transistor.
,
1991,
Science.
[8]
P. Ciampolini,et al.
Numerical analysis of ISFET and LAPS devices
,
1997
.
[9]
Gerhard Klimeck,et al.
Computational aspects of the three-dimensional feature-scale simulation of silicon-nanowire field-effect sensors for DNA detection
,
2007
.
[10]
D. Welch,et al.
Experimental and Simulated Cycling of ISFET Electric Fields for Drift Reset
,
2013,
IEEE Electron Device Letters.
[11]
Tobias Cramer,et al.
Organic ultra-thin film transistors with a liquid gate for extracellular stimulation and recording of electric activity of stem cell-derived neuronal networks.
,
2013,
Physical chemistry chemical physics : PCCP.
[12]
João Abrantes.
Neuroelectronic Interfacing : Semiconductor Chips with Ion Channels , Nerve Cells , and Brain 1
,
2002
.
[13]
Francisco Azuaje,et al.
BioMedical Engineering OnLine
,
2005
.
[14]
Stefano Vassanelli,et al.
Transistor records of excitable neurons from rat brain
,
1998
.
[15]
Sergio Martinoia,et al.
Modeling the neuron-carbon nanotube-ISFET junction to investigate the electrophysiological neuronal activity.
,
2008,
Nano letters.
[16]
J. L. Lentz,et al.
An improved electron and hole mobility model for general purpose device simulation
,
1997
.
[17]
H. Voigt,et al.
Numerical modeling and characterization of Electrolyte/Insulator/Semiconductor sensor systems
,
1994
.