2D analytical modeling and simulation of dual material DG MOSFET for biosensing application

Accepted Manuscript Regular paper 2D anAlytical moDeling and SIMULATION of DUal material DG MOSFET for BIOSENSing application Buvaneswari Balakrishnan, Neerathilingam Balasubramanian Balamurugan PII: DOI: Reference:

S1434-8411(18)31219-6 https://doi.org/10.1016/j.aeue.2018.11.039 AEUE 52605

To appear in:

International Journal of Electronics and Communications

Received Date: Accepted Date:

15 May 2018 29 November 2018

Please cite this article as: B. Balakrishnan, N.B. Balamurugan, 2D anAlytical moDeling and SIMULATION of DUal material DG MOSFET for BIOSENSing application, International Journal of Electronics and Communications (2018), doi: https://doi.org/10.1016/j.aeue.2018.11.039

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2D ANALYTICAL MODELING AND SIMULATION OF DUAL MATERIAL DG MOSFET FOR BIOSENSING APPLICATION

Buvaneswari , Balakrishnan1 and Balamurugan ,Neerathilingam ,Balasubramanian2 1

Department of Computer Science and Engineering, K.L.N. College of Engineering, Madurai, Tamilnadu, India-630 612 2

Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India-625 015

1

Author for correspondence

Abstract In the recent times, the performance of MOSFET in the nanoscaled region attains improvisations through several alternative device structures. Amongst many advanced MOSFET structures, the Double Gate (DG) MOSFET is one such structure which mitigates short channel effects because of its excellent scalability. Among the various structures, FETbased biosensors have shown an accelerated growth recently. Even though many analytical models are available for the Dual Material DG (DMDG) MOSFET structures, this work endeavours to introduce an analytical modeling of nanocavity embedded DMDG structure for the first time. The expression for surface potential is obtained by solving the 2-D Poisson’s equation using a parabolic-potential approach. The threshold voltage is determined from the minimum surface potential model. Sensitivity is computed in terms of relative change in the threshold voltage and it is derived using the model. The influence of various device geometrical parameters like length and thickness of the nanocavity on the sensitivity has been investigated. Further, a comparison of the sensitivity of DG MOSFET and DMDG MOSFET has also been made and the derived results are validated against TCAD simulation results.

Key

words:

Biosensor;

Double

Gate

MOSFET;

Dual

Material;

Dielectric Modulation ;Poisson's Equation.

1. Introduction

CMOS has been the heart of the semiconductor industry because of its successful incorporation into the Integrated Circuits (ICs). In the past few decades, the evaluation of CMOS technology has followed the path of device scaling to achieve density, speed and power improvement. As indicated by the Moore’s law [1], the number of transistors inside

the chips doubles every two years because of the shrinking size of MOSFETs. This scaling reduces the source-to-drain spacing, i.e., the channel length of a MOSFET. This leads to one of the most undesirable effects namely Short Channel Effects (SCEs), which in turn reduces the gate threshold voltage and increases the sub-threshold leakage current. As a consequence of these effects, device turn-off will become critical [2]. To mitigate the SCEs, the development of new device designs, technologies and structures is required [3-5]. Also, use of alternative materials like InGaAs as channel materials enhances the mobility of the future CMOS device [6]. For high frequency applications, researchers have recommended the use of High Electron Mobility Transistor (HEMT) [7-8]. Even though many Multigate (MuG) MOSFETs such as Surrounding-gate [9-11], Tri-gate [12-13] and Double-gate [14-15] are proposed as alternatives for bulk MOSFET beyond 45 nm node, the simplest MuG MOSFET device to model is undoubtedly the symmetric Double-Gate (DG) MOSFET. DG structures have exhibited lots of advantages over conventional bulk device structures. The presence of two gates significantly reduces SCEs, improves punch-through properties, permits complete dielectric isolation and reduces junction capacitance. In addition, thin body DG MOSFETs also provide nearly-ideal sub-threshold slope. The reduced junction capacitance and the presence of two channels drastically boosts the speed and drive current of the DG device structures. At First, Dual Material Gate Field Effect Transistor (DMGFET) was suggested by Wei Long and K.K. Chin [16]. The gate of the DMGFET consists of two laterallycontacting materials with different work functions. This novel gate structure takes advantage of material work function difference in such a way that the threshold voltage near the source is more positive than that near the drain, resulting in a more rapid acceleration of charge carriers in the channel and a screening effect to suppress SCEs. The hetero gate is formed by a self-aligned asymmetric spacer process that was explained by Xing Z [17-18]. The gate could be formed by either an asymmetric etch or asymmetric lift-off process [19]. It is seen

that the SCEs in DMGFET structure are diminished because of the step in the surface potential profile which screens the drain potential variations [20-22]. 2. Applications and Advantages of MOSFET-based biosensors The first biosensor was developed by Led and Clark in 1962 which attracted much attention [23]. Due to the higher sensitivity and ability to perform multiple analyte analysis, biosensors are used in rapid medical diagnostics. As a consequence, scientists are interested towards the evolution of biosensor technologies as newer tools. The first practical use of a FET-type biosensor for an assay of penicillin was released in 1980 [23]. The applications of biosensor have proved immense potentiality because of the high demand in the market such as blood glucose level monitoring [24-25], detection of virus [26-27], detection of Prostate Specific Antigen (PSA) [28-29] and monitoring pH level of solutions [30]. Nanoscaled FET biosensors are one of the challenging technologies in the progress of label-free and high-sensitive detection for disease diagnostics. As the extremely scaled MOSFETs possess outstanding electrical properties, they are well-matched to attain the high sensitivity even with a single molecule. Without sacrificing sensitivity, FET biosensors offer many benefits like high speed, low cost with mass production portable instrumentation and easy operation with a small amount of sample. Although lots of inventions and research reports on FET based biosensor modeling have been reported previously [31-38], an in-depth review of the literature reveals that the use of DMDG MOSFET for the detection of biomolecules has not yet been reported. The purpose of this work is to introduce an analytical model of a rapid, high-sensitive, nanoscaled, gate-engineered DG MOSFET-based biosensor for accurate detection of the bio-molecules. This paper focuses on modeling DMDG MOSFET structure as a biosensor. The rest of the paper is organized as follows: In Section 3, the structure and working principle of the device are presented. In Section 4, the analytical

modeling of the device parameters have been discussed. Descriptions of the results have been shown in Section 5. Conclusion is presented in Section 6.

3. Proposed device structure The Nanocavity Embedded Dielectric Modulated DMDG MOSFET for the application as a biosensor is depicted in Fig.1.

Fig.1. Schematic cross-sectional view of DMDG MOSFET-based biosensor with the nanocavity being filled with bio-molecules

Conventional CMOS processes were used to fabricate nanocavity. The silicon body of the MOSFET was formed on a p-doped Silicon-On-Insulator (SOI) wafer that comprises channel, source electrode and drain electrode. A layer of silicon oxide was grown on the channel part of the silicon area to form the gate oxide, followed by the gate electrode. The gate was wet-etched to carve out nanogap underneath the gate electrode. Here, channel length (L) of 100 nm is used; nanocavity is assumed to be located near the source with a length of 25 nm (0-L1) and near the drain with a length of 25 nm (L3-L4). Nanocavity regions would behave like the sensing sites for the label-free detection of bio-molecules. In order to assemble the bio-molecules in the nanocavity, the phenomenon called intermolecular interactions is applied [39]. Without the bio-molecules, the cavity is assumed to be filled with

air. The presence of the `charged bio-molecules like DNA, Protein etc., would alter the flatband voltage of the nanocavity region. The neutral bio-molecules like glucose can modify the capacitance and dielectric constant of the nanocavity region. Due to these dielectric modulation effects, the device undergoes a threshold voltage shift. This shift is termed as the sensitivity of the device. The proposed structure is assumed to have a 2 nm front and back oxide thickness. The nanocavity height is varied from 10 nm to 25 nm. The thickness of the channel is chosen as 12 nm. The front and back gate electrodes are connected and a symmetrical biasing condition is assumed. The p+ and n+ poly gates with work functions of 5.25 eV and 4.17 eV respectively are considered.

4.

Analytical Modeling

By applying certain boundary conditions of the DMDG MOSFET structures, the analytical parameters of a nano-embedded DMDG MOSFET-based biosensor have been derived. 4.1. Surface Potential A 2D Poisson's equation with uniform doping is used to describe the potential field caused by the charge. The potential distribution in the silicon film is denoted in (1),

 2 ( x, y)  2 ( x, y) qN A   x 2 y 2  si

(1)

According to the parabolic approximation, the potential profile along the four regions of the channel is described as,

 ( x, y)   s ( x)  a1 ( x) y  a2 ( x) y 2

0  x  L,0  y  t si

(2)

where  s is the surface potential and a1(x) and a2(x) are the arbitrary constants as a function of x alone. In Fig.1. the first region (0-L1) and the fourth region (L3-L4) are considered to be under nanocavity. The middle portion is sectioned into two regions for the purpose of

analysis. The second region (L1-L2) is under metal-1, i.e., p+ Gate. The third region (L2-L3) is under metal-2, i.e., n+ Gate. The surface potential is described region-wise in (3),

i ( x, y)  si ( x)  ai1 ( x) y  ai 2 ( x) y 2

(3)

where i=1,2,3 and 4,  si (x) is the region-specific surface potential and a11(x), a12(x), a21(x), a22(x), a31(x), a32(x) are the arbitrary constants as a function of x alone. Due to the symmetric nature of the device along the y-axis, the following boundary conditions are useful to solve potential in all the four regions. This boundary is about the continuity of electric field and surface potential. These are described in the subsequent paragraphs. a. Electric flux at the front gate–oxide interface is continuous and hence the following boundary is formed.

i ( x, y ) y

 y 0

C f ,i



( si ( x)  Vgsf ,i )

(4)

si

where εsi is the dielectric constant of the silicon film, si (x) is the potential function along the front gate oxide-silicon interface and Vgs is the gate-source bias. The gate source bias of the four regions and the capacitances of the four regions are mentioned in (5a) – (5f): Under the nanocavity region (near p+ Gate) region, V gsf ,1  vGS   m1   si 

qN f

(5 a)

C gap

Under the nanocavity region (near n+ Gate) region,

V gsf , 4  vGS   m 2   si 

qN f

(5 b)

C gap

Under the gate region (p+ Gate),

Vgsf , 2  vGS   m1   si Under the gate region (n+ Gate),

Vgsf ,3  vGS   m 2   si

(5 c)

(5 d)

Under the nanocavity region (near n+ gate and p+ gate),

C f ,i 

 bio ox  bioTox   oxTbio

(5 e)

Where i =1,4; Under the gate region (p+ gate and n+ gate)

C f ,i   ox / t ox

(5 f)

where i=2,3; Φm1 ,Φm2 and Φsi are the work functions of the p+ gate , n+ gate and silicon respectively. Cf,i is the capacitance of the nanocavity (or) gate region, N f is the charge density of the bio-molecules,  bio and Tbio are the dielectric constant of the biomolecules and thickness of nanocavity respectively.  ox and Tox are the dielectric constant and thickness of the gate oxide respectively. b. Electric flux at the back gate–oxide and back channel interface is continuous.

i ( x, y ) y



C b ,i

y t si



(Vgsb,i  bi ( x))

(6)

si

where bi (x) is the potential function along the back gate oxide–silicon interface and Cb,i is the capacitance at the back gate. As symmetrical biasing is assumed, bi (x) =  si (x) and Vgsb,i = Vgsf,i. It is assumed that the front gate and back gate oxide capacitances are equal. c.Surface potential at the interface of the two dissimilar gate regions of the front gate is continuous.

s1 ( L1 )  s 2 ( L1 )

(7 a)

 s 2 ( L2 )   s 3 ( L2 )

(7 b)

 s3 ( L3 )   s 4 ( L3 )

(7 c)

d. Electric flux at the interface of two different regions of the front gate is continuous.

 ' s1 ( L1 )   ' s 2 ( L1 )

(8 a)

 ' s 2 ( L2 )   ' s 3 ( L2 )

(8 b)

 ' s 3 ( L3 )   ' s 4 ( L3 )

(8 c)

e. The potential at the source end is as depicted in (9).

 s1 (0)  Vbi

(9)

f. The potential at the drain end is shown in (10).

s 2 ( L)  Vbi  VDS

(10)

where Vbi = VT ln (NAND/ni2) is the built-in potential across the body-source junction ,VT is the thermal voltage, ni is the intrinsic carrier concentration and N A and ND are the doping concentration of the acceptor and the donor respectively. The arbitrary constants a11(x),a12(x),a21(x),a22(x),a31(x),a32(x) can be found from boundary conditions (4)-(5). Substituting these constants in (3) and then in (1), The second order partial differential equation formed as a function of surface potential is given by (11).   C b ,i  C f ,i   si " ( x)   si ( x) C C  C f ,i  1  f ,i  b ,i C si 2C si  C b ,iV gsb,i  C f ,iV gsf ,i 1 2 C f ,i C b ,i  C f ,i C si t si 1  C si 2C si

 qN A   si    C f ,i C b ,i  C f ,i  1   C si 2C si 

(11)

The calculated potential is substituted in the Poisson's equation to resolve the region-wise surface potential. The following are the expressions of surface potential of the proposed DMDG MOSFET biosensor, For region 1, 0  x  L1 ,0  y  t si

  s1 ( x)  A exp( 1 x)  B exp(   1 x)  1 1

(12 a)

For region 2, L1  x  L2 ,0  y  tsi

 s 2 ( x)  C exp(  2 ( x  L1 )  D exp(   2 ( x  L1 ))  2 2

(12 b)

For region 3, L2  x  L3 ,0  y  tsi

  s 3 ( x)  E exp(  3 ( x  L2 ))  F exp(   3 ( x  L2 ))  3 3

(12 c)

For region 4, L3  x  L4 ,0  y  tsi

  s 4 ( x)  G exp(  4 ( x  L3 ))  H exp(   4 ( x  L3 ))  4 4 where the values of  and  for all the four regions are mentioned in (13a) – (13b).   C b ,i  C f ,i  i   C C  C f ,i  1  f ,i  b ,i C si 2C si 

 si

1

C f ,i C si



(13 a)

(13 b)

qN A

i 

     

(12 d)

C b ,i  C f ,i 2C si



Cb ,iV gsb,i  C f ,iV gsf ,i 1 2 C f ,i C b ,i  C f ,i C si t si 1  C si 2C si

The constants A,B,C,D,E,F,G,H are obtained by using the boundary conditions (e) -(f). The derivation to find these constants is enclosed as APPENDIX. 4.2. Electric Field Electric field is a significant design parameter that dictates the immunity of the device towards SCEs. In this work, the electric-field distribution is considered as a crucial parameter to understand the severity of Drain Induced Barrier Lowering (DIBL) effect. The field along the channel length can be determined for the various regions by differentiating the surface potential and thus the resultant Electric field can be written as in (14a) – (14d).

For region 1, 0  x  L1 ,0  y  t si E1 

d s1 ( x, y ) dx

 A 1 exp( 1 x)  B 1 exp(   1 x)

(14 a)

y 0

For region 2, L1  x  L2 ,0  y  tsi E2 

d s 2 ( x, y ) dx

 C  2 exp(  2 ( x  L1 )  D  2 exp(   2 ( x  L1 ))

(14 b)

y 0

For region 3, L2  x  L3 ,0  y  tsi E3 

d s 3 ( x, y ) dx

 E  3 exp(  3 ( x  L2 )  F  3 exp(   2 ( x  L2 ))

(14 c)

y 0

For region 4, L3  x  L4 ,0  y  tsi E4 

d s 4 ( x, y ) dx

 G  4 exp(  4 ( x  L3 )  H  4 exp(   4 ( x  L3 ))

(14 d)

y 0

4.3 Threshold Voltage Threshold voltage is defined as the minimum voltage applied between the gate and the source in order to turn the device on. Yuh-Sheng Jean et. al [40] proposed the thresholdvoltage model of MOSFET devices. In their work, surface potential was obtained by solving the two-dimensional (2-D) Poisson's equation. The analytic threshold voltage model was derived from the minimum surface potential. It was verified that the model accurately predicts the threshold voltage for both fresh and damaged devices. In continuation of this report, many researchers have applied the minimum surface potential to derive the threshold voltage. Recently, this method has also been used to obtain the threshold-voltage of a Junction Less MOSFET for biosensing applications [22]. In this work, the minimum surface potential is derived for the four regions. The rate of change of the potential is equated to zero to find the minimal point. Finally, the minimum surface potential is equated to twice of bulk

potential to obtain the threshold voltage expression. The region-wise threshold voltage values are depicted in Equations (15a) -(15d). For region 1, 0  x  L1 ,0  y  t si Vthreshold,1

  B2 B  qN A  C si t si 2     1    2 F    2 A 2    si  2C f ,1 

(15 a)

For region 2, L1  x  L2 ,0  y  tsi   D2 D  qN A  C si t si 2 Vthreshold, 2    2    2 F     2C 2   si  2C f , 2  For region 3, L2  x  L3 ,0  y  tsi   F2 F  qN A  C si t si 2 Vthreshold,3    3    2 F     2E 2   si  2C f ,3 

(15 b)

(15 c)

For region 4, L3  x  L4 ,0  y  tsi  H2 H Vthreshold, 4    4    2 F 2 G 2  

2  qN A  C si t si      si  2C f , 4

(15 d)

The minimum among these four values is considered as the threshold voltage of the DG MOSFET-based biosensor.

4.4 Sensitivity In specific terms, sensitivity is defined as the relative variation in sensor characteristics when the target molecules attach in the nanogap region. The change in threshold voltage before and after the' immobilization of the bio-molecules is considered to be the sensing metric parameter and is defined as shown in (16a) and (16b) respectively [22], Sensitivity of a Biosensor when Neutral Molecules immobilized

S  Vthreshold K 1  Vthreshold K 1

(16 a)

Sensitivity of a Biosensor when Charged Molecules immobilized

S  Vthreshold N

f

0

 Vthreshold N

(16 b) f

0

where ∆S is the sensitivity of the biosensor, Nf is the charge of the charged biomolecules and K is the dielectric constant of the neutral biomolecules.

5. Results and Discussion Surface potential, electric field, threshold voltage and sensitivity of the DMDG MOSFET-based biosensor have been calculated and plotted. The proposed model is compared with the results of the DG MOSFET-based biosensor. Table 1 summarizes the typical dimensions that are used during the analytical calculation as well as simulation. Sub-. Sections 5.1-5.4 present a detailed discussion of the results. Table. 1. Typical dimensions used for Dual Material DG MOSFET based biosensor structures Parameter Value Thickness of the front/back gate oxide t ox 2 nm Thickness of the channel t si 12 nm Length of the Channel L 100 nm Length of the nanocavity Lnano 10 nm-25 nm Thickness of the nanocavity t nano 10 nm - 25 nm Channel length under nano cavity L1 (or) L4 25 nm Channel length under p+ gate L2 25 nm + Channel length under n gate L3 25 nm Source/Drain Doping N A 1015 cm-3 Body Doping ND 5x1019 cm-3 + Gate Work Function p 5.25 eV Gate Work Function n+ 4.17 eV Charge of biomolecule N f -10x1016 - +10x1016 C/m2 Dielectric constant of biomolecule K 2-12

5.1

Surface potential

Fig.2. Variation of Surface potential versus channel length (from source to drain) for the DM DG MOSFET-based biosensor with the neutral biomolecules. Vds=1 V,Vgs=0 V,L=100 nm,L1=L2=L3=L4=25 nm Fig.2 shows the change in surface potential along the channel length when neutral bio-molecules are applied. The default dielectric constant of a nanogap is assumed as '1' that denotes the absence of the bio-molecules. Dielectric constant values 3,5,7 are used to denote the presence of bio-molecules. The derived model shows evidence of step potential due to the hetero gate structure. As a result of the presence of nanogap cavities at the source and drain end the potential has been decaying under the cavity regions and the lowest surface potential becomes visible at the source-side cavity. It is observable that the DMDG biosensor structure shows a step function in the surface potential along the channel. This outstanding feature screens the region under p+ gate from the drain conductance. From this, it is noticeable that

the DMDG biosensor mitigates DIBL effect. Additionally, the dielectric modulation effect is also severe that ensures rapid detection of the biomolecules.

Fig.3.Variation of Surface potential versus channel length (from source to drain) for the DMDG MOSFET based biosensor with the charged biomolecules. Vds=1 V,Vgs=0 V, L=100 nm,L1=L2=L3=L4=25 nm Fig.3. shows the change in surface potential along the channel length while charged bio-molecules are applied. The presence of charged bio-molcule is realized by assuming the charge values as  10  1016 ,7  1016 C/m2. The charge of zero C/m2 denotes the absence of bio-molecule. The analytically-modeled results are validated against the results of TCAD simulation. The minimum surface potential lies in the nanocavity region. The dual gate encourages a step increase in the surface potential characteristics that ensures the suppression of SCEs. It is observable that the rate of change of surface potential under cavity region is very high as it is directly in contact with the bio-molecules. The surface

potential is modulated heavily when more negative and more positive-charged bio-molecules are applied through the cavity region.

5.2

Electric Field

Fig.4.Electric Field variation along the channel position for DMDG MOSFET biosensor and the DG MOSFET biosensor at the Si-Sio2 interface. Inside: Electric Field variation near the drain end when Vds=1 V,Vgs=0 V, L=100 nm,L1=L2=L3=L4=25 nm Fig.4.shows the comparison of electric field along the channel position of DMDG MOSFET and DG MOSFET biosensors. In both types of biosensors , the highest peak is appearing at 0-10 nm and 80-90 nm of channel region. This is because of the dielectric modulation effect, which is modulating

the work function when bio-molecules are

immobilized in the nano-cavity region. Fig.4. shows the better carrier transport efficiency in case of DM-DG biosensor structures because of three peaks in electric field profile compared to double peak in DG biosensor structures. This characteristic assures for the better average electric field across the channel. The additional peak in the electric field stimulates rapid acceleration to the carriers at the interface of metals and interface of metal - nano-cavity , causing superior carrier transport efficiency to bring more and more carriers to arrive at the

drain. It is worth to note that the electric field near the drain end (i.e., 90-100 nm) is notabley lesser for the DM DG biosensor than the DG biosensor. This lessening of peak electric field at the drain end in DM-DG biosensor structures compared to DG biosensor

structures

ensures raise in average lifetime of the device owing to reduced Hot-Carrier-Effects (HCEs).

5.3

Threshold Voltage

Fig.5 Variation of threshold voltage versus different dielectric constants for the DMDG MOSFET biosensor when neutral biomolecules are immobilized. Vds=1 V,Vgs=0 V, L=100 nm In Fig.5, the threshold voltage variation for the neutral bio-molecules for different device structures with nanocavity dimension has been depicted. From this, it is clear that the DMDG MOSFET structure exhibits a linearly decreasing relationship between the threshold voltage and the dielectric constants. The DG biosensor shifts its threshold voltage in a linear manner only after a certain dielectric constant value. Although Dual Material-based

MOSFET suffers lesser threshold voltage roll-off than the DG biosensor, its threshold voltage is easily modulated by the high-K and low-K bio-molecules. This rate of change in the threshold voltage for a higher cavity dimension is higher. This is because of the fact that huge number of bio-molecules can be immobilized in the nanocavity region with higher dimension. Obviously, an increase in the number of detection molecules increases the turn-on or reactive point of biosensor. A lower threshold voltage in DM DG MOSFET is the root cause for an increased device switching speed.

Fig.6.Variation of threshold voltage for the charged biomolecules for DG and Dual Material based MOSFET based biosensor.with the 10 nm and 25 nm cavity height.when Vds=1 V,Vgs=0 V, L=100 nm. Fig.6 shows the threshold voltage variation versus the charge of the biomolecules for two different cavity heights. The nanocavity height results in an alteration of the voltage which in turn has an effect on the applied gate bias. This variation is dependent

upon the polarity of the charged bio-molecules. In DG and DMDG structures, cavity is formed near both the source and the drain. Because of the dissimilarity of the gate, DG structures have three different regions whereas DMDG gate structures have four regions. Although the threshold voltage roll-off in DMDG structures is considerably less (<0.05 V), the threshold voltage of this structure is mainly modulated by the charges. It is noteworthy that threshold voltage point of a DM structure is lower than that of a DG structure. This is due to the reduced potential coupling ratio as the nanocavity is decreasing the parasitic capacitance between the source/drain and substrate. As a consequence, the DM biosensors are highly immune to SCEs and thus the current-drivability of the biosensor device with DM structure will be increased.

5.4

Sensitivity

Fig.7.Variation of sensitivity with different dielectric constants for the DMDG MOSFET and DG MOSFET based biosensor by varying cavity length when Vds=1 V,Vgs=0 V, L=100 nm.

Fig.7 compares the sensitivity of a DMDG MOSFET biosensor with that of the DG MOSFET biosensor for two different nanocavity dimensions when neutral bio-molecules are immobilized. If the nanocavity region is large in size, the number of bio-molecule samples that can be accommodated in this region is also high. Obviously, the impact of the bio-molecules on the device performance will be more. There is a very big gap in the sensitivity plots of DMDG structure with cavity height of 10 nm and 25 nm. The slope of a sensitivity curve of DM structure and the DG structure is about 50 mV when nanocavity dimension is 10 x 10 nm. With a cavity dimension of 25 x 25 nm, the low-to-high change in the dielectric constant has shown a 70% improvement in the sensitivity of a DG MOSFET biosensor. In case of DMDG structure , the change in sensitivity is above 90% for the low-tohigh change in the dielectric constant of neutral bio-molecule. From this design metric, DMDG MOSFET-based biosensors are treated as the optimal biosensor with higher sensitivity. Hence, the dimension of the nanocavity plays a vital role in the modelling of future MOSFET- based biosensors. Fig.8 compares the sensitivity of DMDG and DG MOSFET biosensors for two different nanocavity dimensions when charged bio-molecules are filled in the nanocavity. It is clear that the increase in the charge of a bio-molecule is linearly modulating the sensitivity. Similarly, cavity height tends to increase the sensitivity of the biosensor. The DMDG structure provides better sensitivity even in the smaller cavity dimension. Even otherwise, the proposed dual material biosensor performs in a superior fashion in comparison with other structures.

Fig.8.Variation of sensitivity with different charges of the charged biomolecules for the DMDG MOSFET and DG MOSFET based biosensor by varying cavity length when V ds=1 V,Vgs=0 V, L=100 nm. 6. CONCLUSION In this analytical model, a novel approach to the modeling of the sensitivity of a Dual Material DG MOSFET structure for the application as a biosensor has been put forward and the results have been validated against the results of TCAD. Also, the results are compared with those of the previous work. The impact of the charged and neutral biomolecules in the device parameters like surface potential, threshold voltage and sensitivity are analytically modeled and discussed. The immunity of the DMDG biosensor to SCEs is also discussed. The threshold voltage of a DM structure goes to prove that it would work even under lower gate bias. Hence, it can be inferred that for a 100 nm biosensor structure, DM

is optimal in terms of less power consumption. These characteristics enable the

proposed structure to mark its significance in the sensor devices in nanoscale dimensions and for the detection of very-low-concentration label-free electrical species.

APPENDIX

    1  2         2  1  Vbi   1  1  Vbi  a1  2  1 2 1 2    1   1  C 1 L1  2e 2

e

1 L1

D

(A.1)

 1 2  1  2         2  1  Vbi   1  1  Vbi  1 2    1   1  1 L1  2e 2

 2 

(A.2)

A=0

B=

(A.3)

1  Vbi 1

(A.4)

 d1 C1 4t  3C1 d1t  3 4  C1 d1  4 R  C1 R  3 4  2C1 d1 4 3

E

2

2

 2C1





3

2

2

2

 3 4

(A.5)

F  C1d1t  C1 R  G d1  1 C1  C1 E

(A.6)

a22 P1  P2  a11a22  a21 E  a13a22  2a23

(A.7)

G

2

2

H  td1  d1 G 2

where, a1  e

1 L1

(A.8)

, b1  e

 2 ( L2  L1 )

, c1  e

3 ( L3  L2 )

, d1  e

 4 ( L4  L3 )

,

P

   34     2 3 1 2  1  2   ,Q  2 3 ,R 3 4 , S  1  Vbi , t  4  Vbi  VDS  3 2 4 1 2  3 4 1

2 a11  C1 E , a13  C1 (d1  1)G , a21  C1 (  4   3 ) ) E , a22  F (  4   3 ) ) ,

2

2

a23  2C1  4 G , a31  C1 E  3 , a32  F  3 , a33  (d12  1)C1  4 G , 2

P1  C1d1t  C1 R , P2  C1  4 R , P3  C1d1  4 t , A11  a11a22  a21 , A12  a13a22  2a23 , P11  P1a22  P2 ,

A21  a11a32  a31 ,

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