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Small-signal model parameter extraction of E-mode N-polar GaN MOS-HEMT using optimization algorithms

本站小编 Free考研考试/2022-01-01




1.
Introduction




An enhancement mode (E-mode) GaN-based high electron mobility transistor (HEMT)[1] is one of the emerging devices of today’s technology for radio frequency integrated circuit (RFIC) design due to its unique material properties and excellent microwave and noise performance[2]. These devices have excellent noise figure, high temperature, high power, high frequency, higher breakdown voltage and good radiation hardness which makes them attractive for low noise applications[3, 4]. Due to a positive threshold voltage and high breakdown voltage E-mode GaN-based low noise amplifiers (LNAs)[5] can avoid a limiter circuit for protection and hence, give rise to a simplified circuit design and improved robustness with reduced cost.



High frequency and low noise characteristics of GaN HEMT can be obtained by reducing the gate length (Lg) and source–drain spacing (Lsd)[4, 6, 7]. But most of the literature does not present the enhancement mode high frequency GaN HEMT because it turns into a depletion mode when its gate length is scaled down[8]. An enhancement mode high frequency low noise GaN HEMT can be obtained by both vertical and lateral scaling of the device[9]. N-polar GaN MOS-HEMT is one of the leading candidates for short channel enhancement mode low noise application[10].



An accurate small-signal equivalent circuit (SSEC) model is required for a wide range of applications starting from understanding its device physics, analysis of device performance, the extraction of intrinsic noise parameters, construction of large-signal models, the characterization and comparison of fabrication processes, and the design of monolithic microwave integrated circuits (MMICs)[11]. Different small-signal models (SSM) have been proposed for GaN HEMT in various works in the literature[12, 13]. So in this paper an accurate small-signal model is developed for the proposed short-channel enhancement mode N-polar GaN MOS-HEMT.



SSEC models for GaN HEMTs are extensively used in the design of RF front end circuits such as a low noise amplifier (LNA) and other RF circuit switches. There are different parameter extraction techniques available in the literature, which can be mainly classified into two categories: direct determination methods[14] and optimization methods[15]. The advantage of numerical optimization methods is that they minimize the error between measured and modeled S-parameters versus different frequencies depending upon different optimization techniques and search range, while the direct method extraction of SSEC model parameters are generally simpler and quicker. In this paper, an improved new analytical method is proposed to extract parasitic capacitances, inductances and resistances. This method is a combination of direct extraction and numerical optimization methods. The advantages of the improved methods are as follows.



(1) The parasitic capacitances, which are treated as depletion-layer extension, are accurately extracted for different pinch-off conditions.



(2) To avoid device performance degradation due to the gate leakage current, the parasitic resistances and inductances are extracted at the pinch-off condition.



(3) These extracted parameters are treated as the initial values for optimization in Matlab and advanced design system (ADS) for more accurate results.



In this paper a short-channel high frequency enhancement mode N-polar GaN MOS-HEMT structure is presented[10]. From the device simulation results an accurate small-signal equivalent circuit model is developed by using the above described methods, which can be suitable for the RFIC design. The small-signal parameters are first extracted using an analytical technique then the initial values are optimized by using different optimization algorithms such as the Quasi Newton method[16], PSO method[17] and Firefly algorithm[18]. Then the optimized small-signal equivalent circuit is simulated using commercially available Keysight’s ADS tool to verify the accuracy of small-signal model parameters. In order to verify the developed model, the small-signal model results are compared with Silvaco TCAD simulation results as well as the available experimental data of 112 nm N-polar GaN MOS-HEMT from the literature. The current gain of the SSEC model is also validated with experimental data from the literature for validation of the developed model[10]. The Firefly based optimization technique is evolved as a leading optimization algorithm to solve different engineering problems[18]. To the best of the author’s knowledge, for the first time the Firefly algorithm has been applied for small-signal model parameters optimization for GaN based MOS-HEMT structure and compared with other optimization techniques, which shows superior performance as compared to previously described optimization algorithms[1618].



The organization of this paper as follows. In Section 2, the device structure of the E-mode N-polar GaN MOS-HEMT device structure along with its SSEC model is presented. A brief description of small-signal parameter extraction is presented in Section 3. In Section 4 an overview of the Firefly optimization algorithm is described. In Section 5 the optimization techniques of small-signal model parameters using different optimization algorithms are described and compared. S-parameters and current gain simulation results of the small-signal model are compared with TCAD device simulation results and available experimental data from the literature in Section 6. Finally, the conclusions are drawn in Section 7.




2.
Device structure and small-signal model




Fig. 1 shows the device structure of the enhancement mode N-polar GaN MOS-HEMT with a double deck T shaped metal gate structure[10]. The device structure consists of a 5 nm GaN channel with a 2 nm AlN back barrier, 2.5 nm AlN cap layer, 5 nm unintentionally doped (UID) Al0.40Ga0.60N layer, 30 nm graded AlGaN modulation doped back barrier and GaN buffer grown on SiC substrate from top to bottom. For reducing EOT, the gate dielectric layer consists of a stack of two high-k dielectric layers of 2.5 nm ZrO2 and 2 nm HfO2. The output conductance, which is the critical parameter for analog circuit design, can be reduced by using a graded AlGaN modulation doped back barrier. By removing the AlN layers from both the source and drain access regions, 2DEG is maintained under both the sidewalls.






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Figure1.
(Color online) Schematic diagram of N-polar GaN MOS-HEMT with 115 nm gate length.




The double deck T-shaped gate consists of a narrow bottom footprint of 115 nm, middle footprint of 300 nm and wide head of 0.9 μm. This double decked T-structure gate helps in lowering parasitic capacitance and resistance thereby reducing both noise figure and noise resistance. The gate metal consists of stack of Pt/Au/Ni. The source to gate spacing (LSG) and drain to gate spacing (LDG) are 1.5 and 1 μm respectively.



The E-mode operation of the device is realized due to depletion of the 2DEG by top AlN layer, depletion formed due to trapped charges of gate oxide and surface depletion. The sidewall passivation layer consists of SiNx of 40 nm to reduce the gate leakage current and hence noise.



For simulation of the short channel N-polar GaN MOS-HEMT high field saturation model, Fermi statistics model and tunneling models are used. SRH (Shockley–Read–Hall) and Auger recombination models are also taken into account.



Fig. 2 shows that the small-signal equivalent circuit[19] of the device consists of both extrinsic and intrinsic elements. The extrinsic parameters consist of bias independent parasitic resistances (Rs, Rd and Rg), parasitic electrode inductances (Ls, Ld and Lg) and parasitic pad capacitances (Cpd, Cpg, Cpgd).






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Figure2.
Small-signal equivalent circuit of N-polar GaN MOS-HEMT.




Similarly, the intrinsic parameters consist of bias dependent parameters Cgs, Cgd, Cds, Rin, Rgd, Gm, gds, τ, Ggsf, and Ggdf, where Cgs, Cgd, Cds represent the intrinsic gate capacitance between gate–source, gate–drain and drain–source respectively. Rin and Rgd represent the intrinsic channel resistance and charging resistance. Gm and gds represent the input and output conductance respectively. In order to characterize the gate leakage current the gate forward and breakdown conductance (Ggsf and Ggdf) are included in the model.




3.
Small-signal parameter extraction





3.1
Extraction of extrinsic parameters




To extract the extrinsic parameters of the SSEC model[12], S-parameters are measured from the device simulation under cold condition i.e. Vds = 0 and Vgs < Vth. The measured S-parameters are converted into Y- and Z-parameters depending upon requirement.




3.1.1
Parasitic capacitances



In order to extract the parasitic capacitances, the device is simulated at low frequency from 1 MHz to 4 GHz. Under low frequency the device exhibits purely capacitive behavior[19] as shown in Fig. 3.






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Figure3.
Equivalent circuit of MOS-HEMT at low frequency.




Y-parameters from this equivalent circuit are calculated from S-parameter simulations from which the values of capacitances can be estimated from the following expressions.









$${mathop{
m Im}nolimits} ({Y_{11}}) = jomega left( {{C_{{
m{pg}}}} + {C_{{
m{pgd}}}}}
ight),$$

(1)









$${mathop{
m Im}nolimits} ({Y_{12}}) = {mathop{
m Im}nolimits} ({Y_{21}}) = - jomega {C_{{
m{pgd}}}},$$

(2)









$${mathop{
m Im}nolimits} ({Y_{22}}) = jomega left( {{C_{{
m{pd}}}} + {C_{{
m{pgd}}}}}
ight).$$

(3)



Fig. 4 shows the plot of imaginary Y-parameter variation with frequency up to 4 GHz. It shows a nearly linear behavior in this frequency range. The parasitic capacitances are extracted easily by calculating the slope of these Y-parameters.






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Figure4.
(Color online) Plot of Imag (Yij) versus frequency.





3.1.2
Parasitic pad inductance and resistance



In order to extract the parasitic pad inductance and resistance, the device is operated at high frequency and S-parameter simulation is carried out. At high frequency the effect of parasitic resistance and inductance cannot be neglected and hence the equivalent circuit can be represented as shown in Fig. 5.



The parasitic inductance and resistance can be extracted after de-embedding parasitic capacitance under the cold pinch-off condition. Under this condition the extracted S-parameters are converted into Y- and Z-parameters.



The Z-parameters are calculated from the circuit as shown in Fig. 5 and are multiplied by ω. The imaginary part of the term ωZij can be expressed as






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Figure5.
SSEC at high frequency.










$${mathop{
m Im}nolimits} (omega {Z_{11}}) = left( {{L_{
m s}} + {L_{
m g}}}
ight){omega ^2} - left( {frac{1}{{{C_{
m s}}}} + frac{1}{{{C_{
m g}}}}}
ight),$$

(4)









$${mathop{
m Im}nolimits} (omega {Z_{22}}) = left( {{L_{
m s}} + {L_{
m d}}}
ight){omega ^2} - left( {frac{1}{{{C_{
m s}}}} + frac{1}{{{C_{
m d}}}}}
ight),$$

(5)









$${text{Im}}(omega {Z_{12}}) = {L_{text{s}}}{omega ^2} - frac{1}{{{C_{text{s}}}}}.$$

(6)



Fig. 6 shows the plot between ωIm(ωZij) versus ω2 from a frequency range of 1 to 20 GHz. Now the initial values of Ls, Ld and Lg are extracted from the slope of curves in Fig. 6.






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Figure6.
(Color online) Plot of ωIm(ωZij) versus ω2.




In the next step the real parts of the Z-parameters are multiplied by ω2 and are given as









$${omega ^2}{mathop{
m Re}nolimits} ({Z_{11}}) = {omega ^2}left( {{R_{
m s}} + {R_{
m g}}}
ight),$$

(7)









$${omega ^2}{mathop{
m Re}nolimits} ({Z_{22}}) = {omega ^2}left( {{R_{
m s}} + {R_{
m d}}}
ight),$$

(8)









$${omega ^2}{mathop{
m Re}nolimits} ({Z_{12}}) = {omega ^2}{R_{
m s}}.$$

(9)



Fig. 7 shows the plot between ωRe (Zij) versus ω2 for a frequency range of 1 to 20 GHz.






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Figure7.
(Color online) Plot of ω2Re (Zij) versus ω2.




Now the extrinsic parasitic resistances (Rs, Rg and Rd) can be extracted from the slope of the above described parameters as shown in Fig. 7.




3.2
Extraction of intrinsic parameters




In order to extract the intrinsic parameters, the method described by Jarndal et al.[20] is applied, where the S-parameters are measured under the normal working bias condition (Vgs > Vth and Vds > 0). The measured S-parameters from the device simulation are converted into Y-parameters. The bias dependent intrinsic parameters are obtained from these Y-parameters by de–embedding the extrinsic parasitic parameters as in the following Eqs. (10)–(13). Since capacitive terms vanish at low frequency, Ggsf and Ggdf can be obtained from Y11 and Y22. Then by separating the real and imaginary part of the Y-parameters, the intrinsic elements can be obtained as









$${Y_{11}} = frac{1}{{{R_i} + displaystylefrac{1}{{{G_{{
m{gsf}}}} + jomega {C_{{
m{gs}}}}}}}} + frac{1}{{{R_{{
m{gd}}}} + displaystylefrac{1}{{{G_{{
m{gdf}}}} + jomega {C_{{
m{gd}}}}}}}},$$

(10)









$${Y_{12}} = - frac{1}{{{R_{{
m{gd}}}} + displaystylefrac{1}{{{G_{{
m{gdf}}}} + jomega {C_{{
m{gd}}}}}}}},$$

(11)









$${Y_{21}} = frac{{{g_{
m m}}{{
m e}^{ - jomega {tau _{
m m}}}}}}{{{R_i}{G_{{
m{gsf}}}} + jomega {C_{{
m{gs}}}}{R_i} + 1}} - displaystylefrac{1}{{{R_{{
m{gd}}}} + frac{1}{{{G_{{
m{gdf}}}} + jomega {C_{{
m{gd}}}}}}}},$$

(12)









$${Y_{22}} = {G_{{
m{ds}}}} + frac{1}{{{R_{{
m{gd}}}} + displaystylefrac{1}{{{G_{{
m{gdf}}}} + jomega {C_{{
m{gd}}}}}}}} + jomega {C_{{
m{ds}}}}.$$

(13)




3.3
RF performance metrics




One of the important figures of merit of RF performance metrics is the unity-current gain cut-off frequency. This can be derived using scattering parameters. The current gain (h21) can be expressed as









$$left| {{h_{21}}}
ight| = 10log left( {frac{{2{S_{12}}}}{{left( {1 - {S_{11}}}
ight)left( {1 + {S_{12}}}
ight) + {S_{12}}{S_{21}}}}}
ight){
m {dB}}.$$

(14)



The frequency at which the current gain drops down to 0 dB is represented as the cut-off frequency and can be expressed in terms of small-signal parameters as









$${f_{
m T}} = frac{{{g_{
m m}}}}{{2pi left( {{C_{{
m{gs}}}} + {C_{{
m{gd}}}}}
ight)left[ {1 + left( {{R_{
m s}} + {R_{
m d}}}
ight){g_{{
m{ds}}}}}
ight] + {C_{{
m{gd}}}}{g_{
m m}}left( {{R_{
m s}} + {R_{
m d}}}
ight)}}.$$

(15)




4.
Firefly based optimization technique




The Firefly optimization algorithm, developed by Yang[18] is based on the flashing behavior and the food searching process of fireflies during the night. The detailed optimization algorithm is described in Ref. [18]. In order to optimize different SSEC parameters the following six steps have been used in this work.



Step 1: Initialization



The different SSEC parameters or variables considered in this optimization algorithm are continuous in nature. The search space of the algorithm consists of fireflies which are the design variables (SSEC parameters). The positions of fireflies are initialized by random generation following the equation as









$${x_i} = {left( {{x_i}}
ight)_{
m L}} + {
m rand}left{ {{{left( {{x_i}}
ight)}_{
m U}} - {{left( {{x_i}}
ight)}_{
m L}}}
ight},$$

(16)



where rand represents the uniformly distributed random number between 0 and 1. xi (xi)U and (xi)L represent the individual firefly and the upper and lower range of the control variable respectively.



Step 2: Calculation of light intensity of fireflies



The next step of initialization of variables is to measure the objective function (fitness) using these variables. In biological terms it represents the light intensity of the fireflies. The objective function considered in this work is to minimize ε (error between TCAD device simulation and SSEC model results) which is described in Section 5.



Step 3: Ranking of Fireflies



In the next step all the fireflies are arranged according to light intensity either in ascending or descending order depending on the nature of optimization. In this work we need to minimize the error, hence the sorting is performed in ascending order. The fireflies which are present at the top are considered to be best for iteration.



Step 4: Updating the position of Fireflies



The different positions of the fireflies are updated as per the procedure described in Ref. [18].



Step 5: Checking the limits



The fireflies may move to new positions after updating the Firefly position. So the constraints of the objective function must be considered and need to be checked every time after updating the position. The constraint used in this work is ε < ε0.



Step 6: Display of the result



Out of all iterations the best solution (position of Firefly) is obtained corresponding to the optimized value of the objective function.




5.
Optimization of SSEC model parameters




Finally all the 19 parameters of the SSEC model are extracted with the above described improved parameter extraction method. Then these values are used as the initial value for various optimization techniques such as the Firefly algorithm, Quasi Newton method and PSO method to obtain accurate values of S-parameters for the entire range of frequency between 1 to 20 GHz. The objective function for optimization is defined as the sum of square errors between measured and simulated S-parameter values[21]. The objective function for our model is defined as follows.









$${varepsilon _{ij}}= frac{{left| {{mathop{
m Re}nolimits} left( {delta {S_{ij}},n}
ight)}
ight| + left| {{mathop{
m Im}nolimits} left( {delta {S_{ij}},n}
ight)}
ight|}}{{{w_{ij}}}},,i,,,j = 1,,,2,,{
m{and}},,n = {
m{ }}1, ,2, ldots N,$$

(17)









$${w_{ij}} = max left| {{S_{ij}}}
ight|, ,, i,,, j = 1, ,, 2, ,, i ne j,$$

(18)









$${w_{ii}} = 1 + max left| {{S_{ii}}}
ight|,i,j = 1,2.$$

(19)



$delta {S_{ij,n}}$ is the difference between the simulated device and model S-parameters at operating frequency. wij is the weighting factor and N is the number of measured data point.



The scalar S parameter fitting error εs is defined as[21]









$${varepsilon _{
m s}} = frac{1}{N}sumlimits_{n = 1}^N {left| {varepsilon left( {{f_n}}
ight)}
ight|} ,$$

(20)



where









$$varepsilon left( {{f_n}}
ight) = left[ {begin{array}{*{20}{c}}{{varepsilon _{11}}left( {{f_n}}
ight)} & {{varepsilon _{12}}left( {{f_n}}
ight)}{{varepsilon _{21}}left( {{f_n}}
ight)} & {{varepsilon _{22}}left( {{f_n}}
ight)}end{array}}
ight].$$

(21)



Stability factor and amplifier gain are the important design parameters for power amplifier design. These factors are expressed in terms of S-parameters as follows. For stability factor and gain, the fitting error is given as[22]









$${varepsilon _{
m k}} = frac{1}{N}sumlimits_{n = 1}^N {left| {{K_{{
m{dev}}}} - {K_{{
m{model}}}}}
ight|} ,$$

(22)









$${varepsilon _{
m g}} = frac{1}{N}sumlimits_{n = 1}^N {left| {{G_{{
m{dev}}}} - {G_{{
m{model}}}}}
ight|} ,$$

(23)



where Kdev and Gdev are the stability factor and gain respectively, which are obtained from the S-parameter simulation of the device. Similarly, Kmodel and Gmodel are the stability factor and gain respectively, which are obtained from the simulation of the SSEC model by considering the extracted initial values. Finally, the objective function for our optimization algorithm is given as









$$varepsilon = frac{1}{3}left( {varepsilon _{
m s}^2 + varepsilon _{
m k}^2 + varepsilon _{
m g}^2}
ight).$$

(24)



This is the main objective of the optimization algorithm, which needs to be minimized.



An iterative flow chart for the whole SSEC model parameter optimization technique is represented in Fig. 8.






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Figure8.
Flowchart for SSEC parameter extraction technique.





6.
Results and discussion




In order verify the small-signal model of the proposed device, initially the DC characteristics such as IdVgs and gmVgs of the device are compared with the available experimental data[10]. Fig. 9 shows the comparison between TCAD simulation and experimental data[10] of drain current and transconductance variation with gate voltage of the device. It shows a positive threshold voltage of 1.6 V and maximum transconductance of 0.55 S/mm.






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Figure9.
(Color online) Comparison of input transfer characteristics between TCAD simulation and experimental data[10] at Vds = 4 V.




Fig. 10 shows the comparison between the TCAD simulation result with experimental data[10] for IdVds characteristics for different gate voltages, which show the maximum drain current density of 1.4 A/mm at Vgs = 5.5 V. The IdVds characteristics show better saturation behavior as compared to the other short channel GaN MOS-HEMT reported in Ref. [9].






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Figure10.
(Color online) Comparison of output characteristics between TCAD simulation and experimental data[10] for different gate voltages.




By using extracted parameters as initial values of the SSEC model, different optimization techniques have been applied to these parameters at a certain range to minimize the percentage error between TCAD device simulation and SSEC model result. The S-parameters of the SSEC model are simulated using a commercially available Keysight’s ADS tool.






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Figure12.
(Color online) Comparison of real and imaginary S12 versus different frequency for different optimization techniques.






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Figure13.
(Color online) Comparison of real and imaginary S21 versus different frequency for different optimization techniques.




Figs. 1114 represent the comparison of the real and imaginary parts of S11, S12, S21 and S22 between device simulation and the SSEC model using different optimization techniques respectively. The figures indicate that the Firefly algorithm optimization based SSEC model shows a better match with the device simulation result with an error of 1.3%, which is lowest as compared to other optimization algorithms. The optimized SSEC model parameter values obtained from the Firefly algorithm are listed in Table 1.






Parameter Initial value Search range (min) Search range (max) Firefly algorithm
Rs (Ω) 10 0 20 12
Rd (Ω) 28 0 30 26.395
Rg (Ω) 9 0 30 11.659
Ls (pH) 20 0 50 26
Ld (pH) 51 0 100 60
Lg (pH) 19 0 50 32
Cpd (fF) 44.46 10 100 36.892
Cpg (fF) 46.66 10 100 48.346
Cpgd (fF) 3.6 1 10 2.34
Cgs (fF) 46 1 100 84
Cgd (fF) 6.56 1 20 4.536
Cds (fF) 15 1 30 18
Rin (Ω) 4.33 1 10 2.5
Gm (m?) 550 400 600 486
Rgd (Ω) 80.092 10 200 76.98
gds (mS) 0.74 0 2 0.635
Τ (ps) 1.55 0.1 2 1.265
Ggs (mS) 0.235 0 2 0.156
Ggdf (mS) 0.000 09 0 1 0.000 97





Table1.
Extracted parameters with Firefly algorithm.



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Parameter Initial value Search range (min) Search range (max) Firefly algorithm
Rs (Ω) 10 0 20 12
Rd (Ω) 28 0 30 26.395
Rg (Ω) 9 0 30 11.659
Ls (pH) 20 0 50 26
Ld (pH) 51 0 100 60
Lg (pH) 19 0 50 32
Cpd (fF) 44.46 10 100 36.892
Cpg (fF) 46.66 10 100 48.346
Cpgd (fF) 3.6 1 10 2.34
Cgs (fF) 46 1 100 84
Cgd (fF) 6.56 1 20 4.536
Cds (fF) 15 1 30 18
Rin (Ω) 4.33 1 10 2.5
Gm (m?) 550 400 600 486
Rgd (Ω) 80.092 10 200 76.98
gds (mS) 0.74 0 2 0.635
Τ (ps) 1.55 0.1 2 1.265
Ggs (mS) 0.235 0 2 0.156
Ggdf (mS) 0.000 09 0 1 0.000 97








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Figure14.
(Color online) Comparison of real and imaginary S22 versus different frequency for different optimization techniques.






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Figure11.
(Color online) Comparison of real and imaginary S11 versus different frequency for different optimization techniques.




The current gains of the different optimized SSEC models are plotted in Fig. 15 with respect to different frequency as per Eq. (14). It shows that the Firefly optimization based SSEC model is in good agreement with experimental data[10] as compared to other optimization methods, which validates our developed SSEC model. The cut-off frequency of the device is extracted to be 122 GHz, which is in good agreement with experimental data[10].






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Figure15.
(Color online) Comparison of current gain versus different frequency for different optimization techniques and experimental data[10].




In order to validate the developed Firefly optimized based SSEC model, different S-parameter results are plotted in a Smith chart at a frequency range of 1–20 GHz and are compared with available experimental data of 112 nm N-polar GaN MOSHEMT[23] as shown in Fig. 16. It shows that both are in good agreement with each other, hence validating our developed model.






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Figure16.
(Color online) Comparison of different S-parameters between Firefly optimized SSEC model and experimental data of 112 nm N-polar GaN MOS-HEMT[23] at Vds= 7 V, Vgs= 2 V at a frequency range of 1–20 GHz.





7.
Conclusion




An improved small-signal parameter extraction technique is proposed for 115 nm gate length enhancement mode N-polar GaN MOS-HEMT. Different optimization techniques such as the PSO method, Quasi Newton method and Firefly algorithm are used for optimization of extracted small-signal parameters to minimize the error between TCAD device simulation and SSEC model results. It is concluded that the firefly based optimization approach can accurately extract the small-signal model parameters as compared to other optimization algorithms. The developed model is validated by comparing S-parameters and unity current-gain of the SSEC model with measured data from device simulations and available experimental data of 112 nm N-polar GaN MOS-HEMT over a frequency range of 1 to 20 GHz, which shows a minimum error percentage of 1.3%.




Acknowledgment




This Publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD Scheme of the Ministry of Electronics & Information Technology, Government of India, being implemented by the Digital India Corporation (formerly Media Lab Asia). The authors acknowledge TEQIP-II funding for facilitating Silvaco TCAD and Keysight’s ADS tools for carrying out the research work.



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