Numerical simulation of ISFET structures for biosensing devices with TCAD tools
- Daniele Passeri^{1}Email author,
- Arianna Morozzi^{1},
- Keida Kanxheri^{2} and
- Andrea Scorzoni^{1}
https://doi.org/10.1186/1475-925X-14-S2-S3
© Passeri et al. 2015
Published: 13 August 2015
Abstract
Background
Ion 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.
Methods
The 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.
Results
The electrostatic behaviour (transfer characteristics) of a general BioFET structure has been simulated when the captured target number increases from 1 to 10. The I_{ D } current as a function of the V_{ DS } 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.
Conclusions
We 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.
Background
The integration of biologically active materials, such as molecules (enzymes, antibodies, antigens, proteins or nucleic acids) and/or biological systems (cells, plants, tissues, organs) with Ion Sensitive Field Effect Transistors (ISFETs) is one of the key elements for the fabrication of the class of biosensors referred to as BioFETs. The aim is to build an hybrid functional system, able to couple the unique (bio)receptor system capabilities with an electrical read-out and acquisition system. Silicon Field Effect Transistors (FETs) are nowadays the primitive element of the new generation of biosensors, since BioFETs can be built from the basic ISFET structure by modifying the gate of the transistor or by coupling the gate oxide with biological sensing elements (receptors).
Aiming at the optimization of the design and fabrication processes of BioFETs, the correlation between technological parameters and device electrical response should more directly be obtained by means of an electrical device-level simulation. To this purpose, different approaches have been proposed in literature [1–3] both at device and circuit level [4–6]. In particular, in the approach proposed in [6] the incorporation of a physical model of the electrolyte-insulator-semiconductor (EIS) structure into a numerical device simulator has been carried out. The EIS system equations are coupled with the charge-transport equations and solved self-consistently on the discretized domain, thus resulting in a "custom" simulation tool.
In this work, we rely on the state-of-the-art commercial Synopsys Sentaurus TCAD packages. Sentaurus is a suite of TCAD tools which simulates the fabrication, operation and reliability of semiconductor devices [7]. The Sentaurus simulators use physical models to represent the device fabrication steps and operation, thereby fostering the exploration and optimization of new semiconductor devices. The adoption of TCAD tools reduces technology development time and cost at the same time providing insight into advanced physical phenomena through self-consistent multidimensional modelling capabilities, improving device design, yield, and reliability. However, the direct device level simulation of an electrolyte solution in Sentaurus TCAD is not straightforward: actually, the suite of standard materials does not include any electrolyte.
Methods
where α and β are material dependent parameters and Eg(0) is the bandgap energy at T = 0K. We set the Eg(0) = 1.5eV thus satisfying the requirement$\left({E}_{g}/2-q\phi \right)\gg kT$, i.e. greater than a few thermal energies (q is the elementary charge and φ is the electrical potential of the material). With this approximation the Poisson-Boltzmann (PB) equation, describing the charge distribution in the electric double layer, can be viewed as the semiconductor equation applied to an intrinsic material [8]. By replacing the electrolyte solution with an intrinsic semiconductor, the electrostatic solution of the electrolyte region can therefore be calculated by solving the semiconductor equations within this region.
The Shockley-Read-Hall (SRH) statistics has been adopted for the generation/recombination processes modelling, by setting the maximum recombination time according to literature findings [9]. In order to account for the surface effects on the carrier mobility, the simplified Lombardi model was used [10]. Actually, in the channel region of a FET, the high transverse electric field forces carriers to interact strongly with the semiconductor-insulator interface. Carriers are subjected to scattering by acoustic surface phonons and surface roughness. This model can describe the mobility degradation caused by these effects; the maximum mobility values have been set to ${\mu}_{p}^{max}=4.98\cdot 1{0}^{-4}\mathsf{\text{c}}{\mathsf{\text{m}}}^{2}/\mathsf{\text{V}}\cdot \mathsf{\text{s}}$ and to${\mu}_{n}^{max}=6.88\cdot 1{0}^{-4}\mathsf{\text{c}}{\mathsf{\text{m}}}^{2}/\mathsf{\text{V}}\cdot \mathsf{\text{s}}$ respectively, to reproduce the behavior of Na^{ + } and Cl^{ - } ions in a NaCl solution [16]. Actually, the maximum mobility values of ionic species can be freely set as well as simulation input parameters. It should be noticed that the carrier mobility is much lower with respect to standard free carrier mobility of an intrinsic semiconductor, thus consistently miming the behavior of ions in a real ionic solution.
Eventually, different ion concentrations within the solution are correlated to the free carriers within the equivalent semiconductor through the densities of states which can be set as input parameters, according to the pH of the solution.
The effective density of states for electrons in the conduction band and for holes in the valence band are calculated from ${N}_{C}=2{\left[\frac{2\pi {m}_{e}^{\mathsf{\text{*}}}kT}{{h}^{2}}\right]}^{3/2}$ and ${N}_{V}=2{\left[\frac{2\pi {m}_{h}^{\mathsf{\text{*}}}kT}{{h}^{2}}\right]}^{3/2}$ where ${m}_{e}^{*}$ and ${m}_{h}^{*}$ are the effective mass of electrons and holes for density of states calculations, and h is the Planck constant.
The relations (6) and (7) hold for a pH = 7 solution. However, this is not a limiting case. By considering the ionic product of water, the ionic species concentrations can be translated to carrier concentrations, depending on the concentration of the solution. In other words, for any given pH value of an equilibrium state solution at a constant temperature T, it is possible to determine the concentration of n and p of the equivalent intrinsic semiconductor, and therefore the values of N_{ C } and N_{ V }.
Results
ISFET simulation
Device simulation of a general BioFET device
Once assessed the suitability of the methodology, a physically sound modelling scheme of BioFET sensors has been set-up. The label-free electrical biosensors rely on the field effect induced by charges of target biomolecules in an electrolyte environment. In real devices, receptor probes are immobilized on the surface of an electrolyte-insulator-semiconductor system so that the target molecules are bound to the probes by the bio-affinity phenomenon. The localized, fixed charges induce the field effect on the underlying conduction channel that leads to the current modulation. The channel modulation effect induced by irregular charge distribution can scarcely be estimated through analytical methods. Moreover, significant effect have been observed at very low target concentration when only a small portion of the receptor probes is bound to the target molecules. On the other hand, the detailed analysis of the effect of the actual charge distributions could conveniently be obtained through an accurate numerical simulation method [8]. The proposed methodology guarantees the self-consistent modelling of very different types of material regions, such as semiconductor, electrolyte solution and organic molecule regions. In particular, a realistic picture of the charge distribution can be obtained as a cluster of charges on the electrolyte region due to target molecules which are bound randomly to a receptor site. When a binding reaction occurs at a certain position on the surface, a given charge density is localized in that specific position.
In this work, we propose this methodology to devise a BioFET aimed at the study of electrophysiological neuronal activity. It has been already pointed out in the past that ISFET devices can measure the extracellular voltage of a single neuron attached with its cell membrane to the device insulator in an open gate configuration [12–14]. The change of the extracellular voltage induced by the neuron gives rise to an electric field across the insulator that modulates the drain-to-source current of the ISFET [15].
When the affinity reaction occurs at the receptor site (i.e., receptor and target molecule bind creation), a given charge distribution is assigned to each single block. The charge state of each block is assumed to be neutral (Q = 0) before the reaction occurs, even if it is possible to model their actual value (being positive or negative), as a reference state to be compared with the situation when a binding reaction occurs and a charge is localized (Q = Q_{ T }). In this case, a value of $\left|{Q}_{T}\right|=4.8\cdot 1{0}^{-16}C$ has been used. The value (order of magnitude) of the fixed charge has been taken from literature [8] as a reference value of the charge localized when a Streptavidin molecule is sensed on a sensor surface. Due to the p-type Si substrate doping concentration, the opposite polarity of the fixed charge is found to be the most effective on the modulation of the FET electrical behaviour. However, both signs of the localized charge (positive or negative) can be taken into account, representing different localized molecules (e.g. Avidin or Streptavidin ).
The aim is to evaluate the effect of the same amount of charge expected in Silicon NanoWire (SiNW) FET biosensors over the electric potential distribution of the proposed structure (which is much larger in terms of dimensions and distances).
As a general comment, the relationship between current modulation and target number is not linear at all. Actually, the position of the targets has a strong effect: for instance, the localization of few targets turned on can result in a greater current modulation with respect to even a bigger number of grouped active targets. In general, charges localized near the source and drain region (C1 and C10 in the example at hand) are less effective, since the modulation of the channel is mostly affected by the influence of the lateral diffusion of source and drain region implants. Moreover, charges localized near the source (C2, C3) are more efficient in current modulation with respect to charges localized near the drain (C8, C9).
Discussion
The adopted simulation methodology (ISFET and electrolyte simulation with a commercial TCAD tool) aims at devising innovative, large BioFET sensor for neuronal activity monitoring. We adopted a simulation scheme which has been used for the analysis of "conventional" biosensors such as Silicon NanoWires (SiNW) [8] on a larger scale. The goal was to check the suitability of the approach to the study of different structures in different operating conditions. The simulated domain has been therefore suitably tailored. This allowed the comparison between the simulated results (e.g. pH sensitivity, calculated currents) with literature data [9]. Once assessed the parameters of the device and the simulation methodologies, a "segmented" ISFET structure has been devices. This device would be much bigger (hundreds of micro-meters) with respect to SiNW FET. However, in order to retain sufficient spatial resolution for the cell activity monitoring, interdigitated FET structure could be proposed. Organic FETs have been proposed as well, however with intrinsic limitation in spatial resolution [17]. Within this framework, the simulation of the structure sketched in Figure 4 has been carried out. The obtained simulation results foster the application of this "segmented" ISFET on a large scale. Actually, its sensitivity is in agreement with simulation findings obtained in [8], even for small localized charges at bigger distance from the conductive channel. Moreover, some interesting indications have been obtained, e.g. the adoption of a higher biasing reference voltage for the electrolyte solution (e.g. greater than 1 Volt) will allow much easier current measurements, at the same time retaining a good sensitivity.
The final goal of this study would be the proof of concept of the feasibility of an integrated high-precision multi-channel system capable of stimulating neural activity while recording very low-voltage responses as low as tens of microvolts. A lumped-element prototype of this system is currently under study within the framework of an international collaboration including the authors of this paper [18].
Conclusions
In this work we 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. In particular, we concentrate on the analysis of the field effect on the conduction channel of a general BioFET structure that leads to the current modulation due to the fixed charges induced by immobilization of target biomolecules in an electrolyte environment. The channel modulation effect induced by irregular, locally distributed charges can be deeply investigated by means of device-level numerical simulation, as well as the effects of different electrolyte concentrations (pH) on the device sensitivity.
In this way a powerful framework for the design and optimization of biosensor can be devised, thus 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, but 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, and in general even randomly distributed targets are more efficient with respect to locally grouped targets on the current modulation. The effect of the V_{ DS } drain source voltage on the sensitivity of the device, as well as the effect of the different polarization of the electrolyte reference voltage (V_{ REF }) can be studied in detail. In particular, 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.
Declarations
Declarations
Publication of this article was supported by Italian MIUR research project PRIN 2010/2011 ARTEMIDE (Autonomous Real Time Embedded Multi-analyte Integrated Detection Environment).
This article has been published as part of BioMedical Engineering OnLine Volume 14 Supplement 2, 2015: Select articles from the 2nd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2014). The full contents of the supplement are available online at http://www.biomedical-engineering-online.com/supplements/14/S2.
Authors’ Affiliations
References
- Heitzinger C, Klimeck G: Computational aspects of the three-dimensional feature-scale simulation of silicon-nanowire field-effect sensors for DNA detection. J Comp Electron. 2007, 6: 387-10.1007/s10825-006-0139-x. DOI 10.1007/s10825006-0139-xView ArticleGoogle Scholar
- Ringhofer C, Heitzinger C: Multi - scale modeling and simulation of field-effect biosensors. ECS Trans. 2008, 14 (1): 11-DOI 10.1149/1.2956012View ArticleGoogle Scholar
- Heitzinger C, Kennell R, Klimeck G, Mauser N, McLennan M, Ringhofer C: Modeling and simulation of field-effect biosensors (BioFETs) and their deployment on the nanoHUB. J Phys Conf Ser. 2008, 107: 012004-DOI 10.1088/1742-6596/107/1/012004View ArticleGoogle Scholar
- Grattarola M, Massobrio G, Martinoia S: Modeling H+-sensitive FET's with SPICE. IEEE Trans Electron Devices. 1987, ED-34: 813-819.Google Scholar
- Treichei W, Ullrich M, Voigt H, Appei M, Ferretti R: Numerical modeling and characterization of Electrolyte/Insulator/Semiconductor sensor systems. Journal of Analytical Chemistry. 1994, 349 (5): 385-390. 10.1007/BF00326604. June IIView ArticleGoogle Scholar
- Colalongo L, Verzellesi G, Passeri D, Margesin B, Rudan M, Ciampolini P: Numerical analysis of ISFET and LAPS devices. Sensors and Actuators B. 1997, B44: 402-408. JuneGoogle Scholar
- Synopsys SENTAURUS TCAD I-2013.12. [http://www.synopsys.com]
- In-Young Chung, Hyeri Jang, Jieun Lee, Hyunggeun Moon, Sung Min Seo, Dae Hwan Kim: Simulation study on discrete charge effects of SiNW biosensors according to bound target position using a 3D TCAD simulator. Nanotechnology. 2012, 23: 065202-10.1088/0957-4484/23/6/065202.View ArticleGoogle Scholar
- Welch David, Shah Sahil, Ozev Sule, Christen Jennifer Blain: Experimental and Simulated Cycling of ISFET Electric Fields for Drift Reset. IEEE Electron Device Letters. 2013, 34 (3): MarchGoogle Scholar
- Darwish MN, et al: An Improved Electron and Hole Mobility Model for General Purpose Device Simulation. IEEE Transactions on Electron Devices. 1997, 44 (9): 1529-1538. 10.1109/16.622611.MathSciNetView ArticleGoogle Scholar
- Bandura AV, Lvov SN: The Ionization Constant of Water over a Wide Range of Temperatures and Densities. J Phys Chem Ref Data. 2006, 35: 15-30. 10.1063/1.1928231.View ArticleGoogle Scholar
- Fromherz P, Offenhausser A, Vetter T, Weis J: Neuron-silicon junction: a Retzius cell of the leech on an insulated-gate field-effect transistor. J A Science. 1991, 252: 1290-1293.Google Scholar
- Vassanelli S, Fromherz P: Transistor-Records of Excitable Neurons from Rat Brain. Appl Phys A: Mater Sci Process. 1998, 66: 459-464. 10.1007/s003390050695.View ArticleGoogle Scholar
- Fromherz P: Neuroelectronic Interfacing: Semiconductor Chips with Ion Channels, Nerve Cells, And Brain. Nanoelectronics and Information Technology. Edited by: Waser, R. 2003, Wiley-VCH: Berlin, Germany, 781-810.Google Scholar
- Giuseppe Massobrio, Paolo Massobrio, Sergio Martinoia: Modeling the Neuron-Carbon Nanotube-ISFET Junction to Investigate the Electrophysiological Neuronal Activity. NANO LETTERS. 2008, 8 (12): 4433-4440. 10.1021/nl802341r.View ArticleGoogle Scholar
- Koneshan S, Rasaiah JC, Lynden-Bell RM, Lee SH: Solvent structure, dynamics, ion mobility in aqueous solutions at 25 °C. J Phys Chem B. 1998, 102 (21): 4193-4204. 10.1021/jp980642x. MayView ArticleGoogle Scholar
- Cramer T, Chelli B, Murgia M, Barbalinardo M, Bystrenova E, de Leeuwb DM, Biscarini F: Organic ultra-thin film transistors with a liquid gatefor extracellular stimulation and recording of electricactivity of stem cell-derived neuronal networks. Phys Chem Chem Phys. 2013, 15: 3897-10.1039/c3cp44251a.View ArticleGoogle Scholar
- Abbati L, Frewin CL, King J, Germano V, Placidi P, Weeber E, Scorzoni A, Saddow SE: A Bidirectional High-Voltage, High-Precision System for Neural Signal Stimulation and Recording. 34th Annual International Conference on Engineering in Medicine and Biology (EMBC) of the IEEE EMBS. 2012, San Diego (CA, USA), Aug. 28 - Sept. 1Google Scholar
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