CHAPTER-4 SIMULATION OF HETROJUNCTION SOLAR CELLS …shodhganga.inflibnet.ac.in/bitstream/10603/42510/8/09... · 2018-07-03 · CHAPTER-4 SIMULATION OF HETROJUNCTION ... semiconductor

CHAPTER-4

SIMULATION OF HETROJUNCTION

SOLAR CELLS WITH AFORS -HET

A number of approaches have been developed to synthesize the CuInSe2 and

CuInGaSe2 based thin film solar cells [1-6]. Due to large numbers of parameters

involve in processing a solar cell such as the energy band gap of the back surface field,

thickness of the emitter, interface density etc. It is a difficult task to scrutinize and

control the effect of each variable on the performance of the solar cell in laboratory. A

method therefore based on computer simulations will be useful to provide a convenient

way to evaluate the role of various parameters present in the fabrication process of the

thin film heterojunction solar cells. A great advantage of computer simulation is its

capacity to study in conditions without loss of materials beyond what is impossible or

difficult experimentally. Computer simulation offers an attractive way of avoiding

practical problems during experiment or synthesis of materials. In this chapter, the

solar cells are computationally fabricated, simulated and optimized using AFORS-HET

(Automat for simulation of hetero-structures) code [7]. The details of this code is

available in ref. [7], however we briefly discuss the silent features of the code in

following sections.

4.1 Introduction

AFORS-HET (Automat for simulation of heterostructures) not only simulates (thin

film) heterojunction solar cells, but one can also observe the analogous measurement

techniques. It is a user – friendly graphical interface allows the visualization, storage

Simulation of heterojunction solar cells with AFORS-HET Chapter-4

77

and comparison of all simulated data. Besides, arbitrary parameter variations and

parameter fits to the corresponding measurements can be performed. In different

experimental situations, one can freely chose a metal/semiconductor or

metal/insulator/semiconductor front contacts to perform different numerical module

based on requirements.

To simulate optical and electrical properties of a solar cell, the one dimensional

semiconductor equations (Poisson’s equation and the transport and continuity

equations for electrons and holes) can be solved by using different numerical models

with the help of finite different conditions i.e.: (a) equilibrium mode (b) steady state

mode (c) steady state mode with small additional sinusoidal perturbation, (d) simple

transient mode, that is switching external quantities instantaneously on/off, (e) general

transient mode, that is allowing for an arbitrary change of external quantities. A

multitude of different physical models has been implemented. The optical models can

be solve either by Lambert-Beer absorption including rough surfaces and using

measured reflection and transmission files or by calculating the plain surface

incoherent/coherent multiple internal reflections, using the complex indices of

reflection for the individual layers [7]. Different recombination models can be

considered within AFORS-HET: radiative recombination, Auger recombination,

Shockley-Read-Hall and/or dangling-bond recombination with arbitrarily distributed

defect states within the bandgap. Super bandgap as well as sub-bandgap generation/

recombination can be treated. For contacts different boundary models can be chosen:

The metallic contacts can be modeled as flatband or Schottky like metal/

semiconductor contacts, or as metal/ insulator/ semiconductor contacts. Furthermore,

insulating boundary contacts can also be chosen.


78

Thus using AFORS-HET all internal cell quantities, such as band diagrams, quasi

Fermi energies, local generation/recombination rates, carrier densities, cell currents and

phase shifts can be calculated. Furthermore, a variety of solar cell characterization

methods can be simulated, i.e.: current voltage, quantum efficiency, transient or quasi-

steady-state photoconductance, transient or quasi-steady-state surface photovoltage,

spectral resolved steady-state or transient photo and electro-luminescence,

impedance/admittance, capacitance-voltage, capacitance-temperature and capacitance-

frequency spectroscopy and electrical detected magnetic resonance. The program

allows for arbitrary parameter variations and multidimensional parameter fitting in

order to match simulated measurements to real measurements [8].

4.2 Modeling capabilities

An arbitrary sequence of semiconducting layers can be modeled, specifying the layer

and if needed interface properties, i.e the defect distribution of states. Using Shockley-

Read-Hall recombination statistics, the one-dimensional semiconductor equations are

solved for thermal equilibrium, various steady state conditions (specifying the external

cell voltage or cell current and the spectral illumination) and for small additional

sinusoidal modulations the external applied voltage/illumination.

The internal cell characteristics, such as band diagrams, local generation and

recombination rates, local cell currents, free and trapped carrier densities can be

calculated. In addition, a variety of characterization methods can be simulated, i.e.

current-voltage (I-V), quantum efficiency (IQE, EQE), surface photovoltage (SPV),

photo-electroluminescence (PEL), impedance spectroscopy (IMP), capacitance-voltage


79

(C-V), capacitance-frequency (C-f) and electrically detected magnetic resonance

(EDMR).

The freely available and developed numerical modules are listed below:

(a) The front contact can be treated either as a metal /semiconductor contact (Schottky

contact) or as a metal/insulator/semiconductor (MIS contact). (b) The transport across

each semiconductor/semiconductor interface can be modeled either by drift-diffusion

currents or by thermionic emission. (c) The optical generation rate can be calculated

taking into account coherent/ incoherent multiple reflections. (d) A specific numerical

module for crystalline silicon considers impurity and carrier – carrier scattering [9, 10].

4.2.1 Optical calculation: super bandgap generation models

To calculate the generation rate Gn(x,t), Gp(x,t) of electrons and holes due to photon

absorption within the bulk of the semiconductor layers, a distinction between super

bandgap generation and sub bandgap generation is developed. The super-bandgap

generation rate is calculated by optical modeling as it is independent of the local

particle densities n(x,t) and p(x,t). Sub-bandgap generation depends on the local

particle densities and has been calculated within the electrical modeling part. The

optical superbandgap generation rate is equal for electrons and holes

G(x,t)=Gn(x,t)=Gp(x,t) which can either be imported by loading an appropriate file

(using external programs for its calculation) or it can be calculated within AFORS-

HET.

So far, two optical models are implemented in AFORS-HET, i.e. the optical model

Lambert- Beer absorption and the optical model coherent/incoherent internal multiple

reflections. The first one takes textured surfaces and multiple internal boundary


80

reflections into account (due to simple geometrical optics) but neglects coherence

effects [7-10]. It is especially suited to treat wafer based crystalline silicon solar cells.

The second takes coherence effects into account, but this is done only for plain

surfaces. If coherence effects in thin film solar cells were applied accordingly.

4.2.1.1 Optical model: Lambert-Beer absorption

The absorption within the semiconductor stack using optical mode can be calculated

assuming simple Lambert-Beer absorption, allowing multiple forward and backward

travelling of the incoming light, however disregarding coherent interference. A

(measured) reflectance and absorbance file of the illuminated contact R(λ), A(λ) have

been loaded. The incoming spectral photon flux φ0 (λ,t) is weighted with the contact

reflection and absorption, i.e. the photon flux impinging on the first semiconductor

layer is given by φ0(λ,t)R(λ)A(λ). To simulate the extended path length caused by a

textured surface, the angle of incidence δ of the incoming light can be adjusted. On a

textured Si wafer with <111> pyramids, this angle is δ = 54.740 , whereas δ=0

0 equals

normal incidence. The angle γ in which the light travels through the layer stack

depends on the wavelength of the incoming light and is calculated according to

Snellius’ law:

,)(

1)sin(arcsin)(

n 4.1

Where, n(λ) is the wavelength dependent refractive index of the first semiconductor

layer at the illuminated side. In this model, the change in γ(λ) is ignored, when the light

passes through a semiconductor/semiconductor layer interface with two different


81

refraction indices. It is assumed that all photons with a specified wavelength cross the

layer stack under a distinct angle γ.

The photon absorption is calculated from the spectral absorption coefficient αx(λ) =

4πk(λ)/λ of the semiconductor layer corresponding to the position x within the stack,

using extinction coefficient k(λ) of the layer. The super bandgap electron/hole

generation rate for one single run through the layer stack is (no multiple passes) given

by:

)cos(

)(max

min

0 )()()(),(),(

x

x

x

eARtdtxG

4.2

The minimum and maximum wavelength λmin, λmax for the integration were generally

divided by the loaded spectral range of the incoming spectral photon flux, φ0(λ,t). Only

super band gap generation is considered, λmax is modified in such order to ensure that

only super bandgap generation is considered: λmax ≤ hc/Eg.

To simulate the influence of light trapping mechanisms, internal reflections at both

contacts have been additionally specified. They can either be set as a constant value or

depending on wavelength (a measured or calculated file can be loaded). The light then

passes through the layer stack several times as defined by user, thereby enhancing the

absorbtivity of the layer stack (the local generation rate). The residual flux after the

defined number of passes is added to the transmitted flux at the contact, at which the

calculation ended (illuminated or not-illuminated contact), disregarding the internal

reflection definitions at this contact. This model was designed to estimate the influence

of light trapping of crystalline silicon solar cells and to adapt the simulation to real


82

measurements. By neglecting the internal multiple reflections and refractions within

the layer stack.

4.2.1.2 Optical model: coherent/ incoherent multiple reflections

This model calculates the absorption within the semiconductor stack by modeling

coherent or incoherent internal multiple reflections within the semiconductor stack. To

model the effect of anti-reflection coatings additional non-conducting optical layers in

front of the front contact/ behind the back contact of the solar cell is assumed. The

reflectance, transmittance and absorbance of all layers (optical layers and the

semiconductor layers) were calculated, using the concepts of complex Fresnel

amplitudes. Each layer has been specified to be optically coherent or optically

incoherent for a particular light beam (incident illumination). A layer is considered to

be coherent if its thickness is smaller than the coherence length of the light beam that is

incident on the system.

In order to consider the coherent effects, the specified incoming illumination φ0(λ,t)

has been modeled by an incoming electromagnetic wave, with a complex electric field

component ),(~

0 tE . The complex electric field components of the travelling wave are

retraced according to the Fresnel formulas, and thus the resulting electromagnetic wave

),,(~

txE at any position x within the layer stack is calculated, while an incoherent

layer is modeled by a coherent calculation of several electromagnetic waves within that

layer (specified by the integer NincoherentIteration), assuming some phase shift between

them, and averaging over the resulting electric field components.


83

4.2.2 Electrical calculation

Within the bulk of each semiconductor layer, Poisson’s equation and the transport

equations for electrons and holes have been solved in one dimension. So far, there are

two semiconductor bulk models are available, i.e. the bulk model” standard

semiconductor” and the bulk model “crystalline silicon”. Using the standard

semiconductor model, all bulk layer input parameters individual, adjusted accordingly.

For silicon bulk model, most input parameters for crystalline silicon are calculated

from the doping and defect densities ND(x), NA(x), Ntrap of crystalline silicon. Thus the

effects like bandgap narrowing or the doping dependence of the mobility or of the

Auger recombination of crystalline silicon are explicitly modeled.

Within each layer, a functional dependence in space has been specified for the doping

densities ND(x), NA(x). These input parameters can be chosen to be (1) constant, (2)

linear, (3) exponential, (4) Gaussian like, (5) error function like decreasing or

increasing as a function of the space coordinate x.

4.2.2.1 Bulk model: standard semiconductor

The doping densities ND (x), NA(x) of fixed donator/ acceptor states at position x

within the cell are assumed to be always completely ionized. In contrast, defects Ntrap

(E) located at a specific energy E within the bandgap of the semiconductor can be

locally charged/ uncharged within the system. Defects have been chosen to be either

(1) acceptor-like Shockley –Read-Hall defects, (2) donor-like Shockley-Read-Hall

defects or (3) dangling bond defects.

Depending on the defect-type chosen, these defects can either be empty, singly

occupied with electrons or even doubly occupied with electrons (in case of the


84

dangling bond defect). Acceptor-like Shockley-Read-Hall defects are negatively

charged or neutral, if occupied and empty, respectively. Donor-like Shockley-Read-

Hall defects are positively charged, if empty, and neutral, if occupied. Dangling bond

defects are positively charged, if empty, and neutral, if singly occupied and negatively

charged, if doubly occupied.

4.2.2.2 Electrical calculation - interfaces: semiconductor/semiconductor interface

models

Each interface between two adjacent semiconductor layers can be described by three

different interface models: (1) interface model:”no interface”, (2) interface

model:”drift- diffusion interface” and (3) interface model:” thermionic emission

interface”. If no interface is chosen, the transport across the interface is treated in

complete analogy to the “drift diffusion” model. The ‘drift diffusion” interface model

considers the transport across the heterojunction interface in the same way as in the

bulk layers, thereby assuming a certain interface thickness. The “thermionic emission”

interface model treats a real interface which interacts with both adjacent semiconductor

layers.

4.2.2.3 Electrical calculation - boundaries: semiconductor/semiconductor interface

models

The electrical front/back contacts of the semiconductor stack are usually assumed to be

metallic, in order to be able to withdraw a current. However, they may also be

insulating in order to be able to simulate some specific measurement methods like for

example quasi steady state photoconductance (QSSPC) or surface photovoltage (SPV).

Till date, four different boundary models for the interface between the contact and the


85

semiconductor adjacent to the contact can be chosen: (1) “flatband

metal/semiconductor contact”, (2) “Schottky metal/semiconductor contact”, (3)

“insulator/semiconductor”. The boundaries serve as a boundary condition for the

system of differential equations describing the semiconductor stack, thus three

boundary conditions for the potential and the electron/hole currents at the front and at

the back side of the stack have to be stated.

4.3 Parameters used for CuInSe2 and CuInGaSe2 solar cell

simulation

In order to numerically model the CuInSe2 and CuInGaSe2 based solar cells, the

variables in the semiconductor transport equations should be correctly given. The

parameters, such as bandgap energy, effective density of states, dielectric constants,

etc., are the properties of the material. Unlike, silicon whose properties is extensively

investigated and standardized, the reported values of CuInSe2 and CuInGaSe2 material

parameters vary in a wide range. Table 4.1 and 4.2 summarize the parameters used in

simulation of these solar cell materials.

In the present work, AFORS-HET code is used for simulation to determine the current-

voltage and photoelectroluminescence characteristic of solar cells. AFORS-HET has

been quite successful in predicting the I-V and photoelectroluminescence

characteristics for variety of heterojunction solar cells [8-9].

4.4 Results and discussion

4.4.1 Copper Indium Diselenide (CIS) based solar cell.

CuInSe2 (CIS) and related materials form a class of materials useful for photovoltaic


86

applications due to their peculiar optical, electrical and structural properties [10]. The

CIS enjoys considerable interest for photovoltaic application because of its suitable

direct bandgap (1.04 eV), high optical absorption coefficient, a moderate surface

recombination velocity and the radiation resistance, which give an opportunity to

fabricate the low cost, stable and highly efficient thin film solar cells [11-15]. Many

investigators have tried to improve the experimental efficiency of the cells, by tailoring

the physical parameters of the participating layers in the thin film solar cells. Also, the

variation of the Cu/In ratio, carrier concentrations, resistivity, thickness of CIS

absorber, phases, etc. play important roles. In the present work, heterojunction of

ITO/CIS/CdS/ZnO:Ag thin film solar cells has been designed and performance is

analyzed by computer simulation [14]. The cell parameters like open circuit voltage

(Voc), short circuit current (Isc), efficiency (η) and fill factor were also evaluated. The

J-V characteristics of the cell are obtained by varying optimum operating temperature.

Table 4.1: The parameters used for CuInSe2 solar cell simulation.

Parameters Standard

Values Reference

Value

used

Band gap (Eg) (eV) 0.95-1.05 [19] 1.05

Electron affinity (chi) [eV] 5.48

4 [20] 4

Dielectric constant (dk) 12

15 [21] 15

Effective conduction band density (Nc) cm-3

1016

-1018

[22] 1E18

Effective valence band density (Nv) cm-3

1016

-1018

[23] 8E18

Acceptor concentration (Na) cm-3

1016

-1018

[24] 9E18

Donor concentration (Nd) cm-3

1016

-1018

[25] 0

Electrons mobility (µn) (cm2/V.s) 10-400 [26] 400

Holes mobility (µp) (cm2/V.s) 10-400 [27] 300


87

Table 4.2: The parameters used for CuInGaSe2 solar cell simulation.

The parameters obtained by varying optimum operating temperature are enlisted in

table 4.3 [16].

The tables revealed that the open circuit voltage (Voc) is the most affected parameters

under temerature. The impact of increasing temperature on Voc is shown in Fig. 4.1. It

shows that the Voc decreases with increase in temperature because of the temperature

dependence of the reverse saturation current. The reverse current is due to the diffusive

flow of minority electrons or holes. Hence Is, reverse saturation current depends on the

diffusion coefficient of electron and holes. The minority carriers are thermally

generated and highly sensitive to temperature change. The reverse saturation current Is

is given as:

Tk

AeEI

B

g

s

4.3

Parameters Values Reference Value used

Band gap (Eg) (eV) 1.05-1.65 [28] 1.4

Electron affinity (chi) [eV] 4.5

4.2 [29] 4.2

Dielectric constant (dk) 13.6

10 [30] 10

Effective conduction band density (Nc) cm-3

1016

-1018

[31] 2.2E18

Effective valence band density (Nv) cm-3

1016

-1018

[32] 1.8E19

Acceptor concentration (Na) cm-3

1016

-1018

[33] 2E16

Donor concentration (Nd) cm-3

1016

-1018

[32] 0

Electrons mobility (µn) (cm2/V.s) 100 [32] 100

Holes mobility (µp) (cm2/V.s) 25 [32] 25


88

Where A is nearly constant independent of temperature and dependent on diffusion

coefficient of holes and electrons. Eg is the bandgap of the semiconductor, kB is the

Boltzmann constant, ʋ is a constant and T is the absolute temperature. The most

significant effect on the solar cell parameter is due to the intrinsic carrier concentration

(ni). Narrowing of bandgap energy may accelerate the recombination of electron-hole

pairs (EHP) between valence and conduction bands. At high temperature, the band gap

energy is unstable which may lead to recombination of electrons and holes across the

regions. Hence slightly decrease in Jsc as shown in table 4.3. Fill factor and efficiency

depends on Voc and Jsc, therefore reduction of Voc on increasing operating temperature

leads to the reduction in fill factor and efficiency [17].

The thickness of the optimum CdS buffer layer was kept within 50nm -60nm [18]. As

the thickness of CdS is increases the Voc and Jsc decreases. This results in higher

photon absorption loss. Due to reduction in Voc and Jsc fill factor of the cell also

decreases. As shown in Table 4.4 as buffer layer increases, more photons which carry

the energy are being absorbed by this layer. Therefore it will lead to the decrease in the

number of photons in the absorber layer. The 0.065 thickness of the CdS µm gives the

efficiency of about 5.19% which is highest.

According to Beer’s law, the thickness of the absorber should be sufficient to absorb

most of the photons from the solar spectrum, when they impinge on the solar cells.

Ninety percent of photons can be absorbed by the film thickness of > 1µm, therefore

1.5 µm-2.5µm thick absorber is enough [34,35]. In ITO/CIS/CdS/ZnO:Ag structure

CIS has been used as an absorber and increase of the thickness of the absorber results

in the improvement in the efficiency of cell.

The thickness of CIS absorber was kept 0.70 μm to 0.95 μm . Table 4.5 shows the

effect of the CIS absorber layer thickness on Voc and Jsc. Both parameters increase with


89

the thickness of CIS absorber layer. Longer wavelength photons will be deeper within

the CIS absorber layer as shown in Fig. 4.3. Thus the efficiency and fill factor

increases on increasing the thickness of CIS absorber increases. The efficiency is best

at the thickness of 0.95 µm CIS solar cell and equal to 6.17 %.

Figure 4.1: J-V Characteristics of ITO/CIS/CdS/ZnO:Ag structure at various temperatures.

Table 4.3: J-V parameters of CIS solar cell at various temperatures.

The role of window layer is also critical in the thin film solar cells in ITO/ZnO: Ag/p-

Cds/n-CIS/Au. The CIS absorber layer in the present calculation is designed onto the

CdS window layer. Table 4.6 enlist the parameters used during simulation process. In

T(K) Voc (mV) Jsc (mA/cm2) FF(%) η (%)

300 682 8.964 82.06 5.017

310 666 8.992 82.11 4.921

320 650 9.019 81.51 4.784

330 635 9.045 75.05 4.312

340 619 9.07 76.69 4.309

350 605 9.094 77.91 4.290


90

present simulation process, the solar radiation AM 1.5 is adopted as the illumination

source with a power density of 100 mW/cm2. The PEL and J-V characteristics of the

cell is given in Figs. 4.4 and 4.5

Figure 4.2: J-V Characteristics of ITO/CIS/CdS/ZnO:Ag structure at various thickness of CdS

Table 4.4: J-V parameters of CIS solar cells at various thickness of buffer CdS.

4.4.1.1 Optical and electrical properties

(i) Photoelectroluminescence

The optical model is obtained by Lambert-Beer is absorption method. The external

illumination of AM 1.5 radiation is adopted as the illumination source with a power

density of 100 mW/cm2. The emitted radiation according to generalized Planck’s

CdS thickness

(µm) Voc (mV) Jsc (mA/cm

2) FF(%) η (%)

0.055 608 9.57 77.78 4.53

0.058 610 9.84 77.74 4.67

0.060 611 10.03 77.63 4.76

0.062 613 10.20 77.53 4.85

0.065 618 10.89 77.24 5.19


91

Figure 4.3: J-V Characteristics of ITO/CIS/CdS/ZnO:Ag structure at various thickness of CIS.

Table 4.5: J-V parameters of CIS solar cells at various thickness of CIS absorber.

equation can be calculated as:

1/()(exp

1.

),(2)(

5

kTxExEhc

xdxcI

FPFn

4.4

Where α is the absorption coefficient, λ is a given wavelength, EFn and EFp is the

quasi-fermi level of electrons and holes and I(λ) is the emitted intensity of photons.

The wavelength dependent emitted intensity to the back and front is calculated by

CIS thickness

(µm) Voc (mV) Jsc (mA/cm2) FF(%) η (%)

0.70 619.5 11.3 82.06 5.40

0.80 622.7 11.69 76.96 5.60

0.85 625.8 12.08 76.71 5.79

0.90 627.3 12.45 76.63 5.98

0.95 628.9 12.81 76.54 6.16


92

integrating over the whole structure, taking photon re-absorption into account. External

illumination or applied voltage that cause quasi-Fermi level splitting are specified.

Furthermore the wavelength region for which the emitted intensity is calculated can be

selected.

Figure 4.4 shows photoluminescence (PL) spectra of ITO/ZnO:Ag/p-Cds/n-CIS/Au

heterojunction solar cell at different temperatures. As shown in Figure 4.4 PEL spectra

at 250 and 275 K show two peaks at the wavelengths 393 nm and 403 nm respectively.

With increase in temperature the intensity of PL spectra also increases. The peaks also

shift towards higher wavelength. This is because the sensitivity towards interface state

densities (Dit) increases as temperature increases. It means spectral response increase

as device temperature increases.

Table 4.6: Parameters used in the simulation of ITO/ZnO: Ag/p-Cds/n-CIS/Au cell.

(ii) Electrical properties

The electrical front/back contacts of the semiconductor stack are usually assumed to be

metallic, in order to be able to withdraw a current. However, they may be insulating in

Parameter p-CIS n-CdS ZnO

Thickness (nm) 1 80 80

Bandgap (eV) 1.05 2.4 1.924

Dielectric constant 15 10 9

Electron affinity 4 4.2 4.4

Electron mobility 400 0 100

Hole mobility 300 0 25

Conduction band density 1E18 2.8E19 2.8E19

Effective valence banddensity 8E18 2.68E19 2.68E19


93

Figure 4.4: PEL spectra of of ITO/ZnO:Ag/p-Cds/n-CIS/Au heterojunction solar cell

under different temperature.

Figure 4.5: Current-voltage simulation under AM 1.5 illumination at illumination

intensity of 100mW/cm2.

order to simulated some specific measurements like quasi steady state photo

conductance (QSSPC) of surface photovoltage (SPV). This measurement is performed

by varying the external voltage at the boundaries and plots the resulting external

current through the semiconductor stalk in order to obtain the current – voltage (Fig.

4.5) characteristic of the simulated structure. For each voltage the total current through

the structure (the sum of the electron and hole current at a boundary grid point) is then


94

calculated. This can be done in the dark or under an illumination. The measurement

model can iterates the specific data points to determine the maximum-power point

(Impp), open –circuit voltage (Voc), short circuit current (Isc) and thus calculate the fill-

factor (FF) and the efficiency (Eff) of the solar cell. The parameters evaluated are

shown in Table 4.6 along with the available experimental results.

mpp mpp

oc sc

V IFF

V I 4.5

scocscoc

mppmpp

IV

FillFactor

IV

IV 4.6

The efficiency of 10.33% is achieved by designing CuInSe2 based heterojunction solar

cell in the present study.

4.4.2 CIGS based solar cell parameters

The Cu(InGa)Se2 (CIGS) based thin film solar cells have earned special interest among

the family of solar cells because their efficiency has been significantly enhanced to

20%. There are several important merits in CIGS for each high efficiency:(i) the

bandgap of CIGS can be varied by varying Ga composition to obtain required band gap

that meets the solar spectrum to absorb most of the photons (ii) in order to make abrupt

junction with window layer, the carrier concentration and resistivity of CIGS can be

varied by controlling its intrinsic composition without using extrinsic dopants. The

thickness of 1.5-2.5 µm for CIGS layer is enough to form CIGS based thin film solar

cells beacuse of its direct bandgap, whereas in the case of Si based solar cells, the Si

needs thicker layers about 250 µm owing to indirect band gap. In present simulation

process, the solar radiation AM 1.5 radiation is adopted as the illumination source with

a power density of 100 mW/cm2 as shown in Fig. 4.6.


95

Table 4.6. J-V parameters of ITO/ZnO:Ag/p-Cds/n-CIS/Au thin film solar cell.

4.4.2.1 Optical and electrical properties of CIGS based solar cell

(i) Photoelectroluminescence

Figure 4.7 shows photoelectroluminescence spectra simulated at various temperature

of the device. On increasing temperature spectral response flux of the device also

increases. Molebedenum is used as a back contact or metallic contact of the cell to

obtain good conductivity. There is an increase in the flux as the operating temperature

of the cell increases it means number of electrons and holes emission is maximum at

400K. The electrical conductivity of the CIGS cell is obtained by keeping device

temperature at 400K.

Figure 4.6: Schematic diagram of the CIGS based thin film solar cell.

Cell structure Voc(V) Jsc(mA/cm2)

Fill

Factor η Growth Reference

ITO/ZnO:Ag/p-Cds/n-

CIS/Au 304 56.8 59.78 10.33 Present

Glass/ZnO:Al/CdS/CIS/Au 350 39.8 58 8.1 CBD 10

Glass/TCO/CdS/CIS/Au 220 32.8 29 2.06 CBD 11


96

Figure 4.7: Photoelectroluminescence of the cell

(ii) Electrical properties

Fig. 4.8 shows the current voltage characteristics of ITO/Mo/CIGS/CdS/ZnO/Al

heterojunction solar cells. The solar cell parameters like open circuit voltage as (Voc)

512 mV, short circuit current is 40 mA/cm2, fill factor is 80.66 and efficiency is

16.52%.

Figure 4.8: I-V characteristics of the cell.


97

4.5 Conclusions

The present chapter describes the results of CuInSe2 and CuInGaSe2 based solar cell

devices using a computer simulation code namely AFORS-HET. We have investigated

the cell parameters like open circuit voltage, short circuit current, efficiency and fill

factor for both CuInSe2 and CuInGaSe2 systems. Our results show that the open circuit

voltage is the most affected parameter with temperature. The Voc decreases with

temperature due to the temperature dependent behavior of reverse saturation current

arising from the diffusive flow of minority electrons or holes. The parameters fill

factor and efficiency depend on Voc and Jsc. The temperature also affects the intensity

of PL spectra, which show increasing trend with increasing temperature. Our

simulation results show that the efficiency is about 10 and 16 % respectively for CIS

and CIGS systems. CdS is used as a buffer layer in CuInSe2 and CuInGaSe2

respectively. For CuInSe2 and CuInGaSe2 based solar cell 10.2 % and 16.5 %

respectively efficiency is achieved. Work is in progress for further improvement in the

efficiency of these devices.


98

References

[1] S. Agilan, D. Mangalaraj, S.K. Narayandas, G. Mohan Rao and S. Velumani,

Vacuum 84, 1220 (2010).

[2] S.R. Kodigala, I. Bhatt, T.P. Chow, J.K. Kimm, E.F. Schubert, D. Johnstone and

S.A. Biyikli, J. Appl. Phys. 98, 106108 (2005).

[3] J. Marlein, K.Decock and M. Burgelman, Thin Solid Films 517, 2353 (2009).

[4] M. Kauk, M. Altosaar, J. Raudoja, A. Jagomagi, M. Danilson and T. Varema,

Thin Solid Films 515, 5880 (2007).

[5] M. Rusu, S. Doka, C.A. Kaufmann, N. Grigorieva, T.S. Niedrig, M.C. Lux-

Steiner, Thin Solid Films 341, 480 (2005).

[6] P. Jackson, R. Wurz, U. Rau, J. Matteis, M. Kurth, T. Schlotzer, G. Bilger and J.H.

Wernerl, Prog. Photovolt. Res. Appl. 15, 507 (2007).

[7] J.A. Abushama, J. Wax, T. Berens, J. Tuttle, 4th

World conference on photovoltaic

Energy Conversion, p. 487 (2006).

[8] T. Minemoto, T. Matsui, H. Takakura, Y. Hamakawa et al., Sol. Ene. Mat. & Sol.

Cells 67, 83 (2001).

[9] J. Marlein, K. Decock and M. Burgelman, Thin solid films 517, 2353 (2009).

[10] I. Repins, M. Contreras, B. Egaas et al., Prog. Photovolt. 235, 16 (2008).

[11] J. Palm, S. Jost, R. Hock, V. Probst, Thin Solid Films 515, 5913(2007).

[12] M.S. Niasari and F. Davar, Mat. Lett. 63, 441(2009).

[13] B. Koo, R.N. Patel, B.A. Korgel, J. Am. Chem. Soc.131, 3134 (2009).

[14] J. Chung, S. J. Kim, Bull. Korean Chem. Soc. 31, 26 95 (2010).

[15] J. Xu, C.S. Lee, Y.B. Tang, X. Chen et al., ACS Nano 4, 1845 (2010).

[16] L. Wan, Y. Cao, D. J. Wang, Mater. Res. 24, 2294 (2009).


99

[17] Y. Okano, T. Nakada, Akio Kunioka, Sol. Ene. Mat. and Sol. Cells 50, 105-110

(1998).

[18] T. Nakada, T. Kume, A. Kunioka, Sol. Ene. Mat. and Sol. Cells 50, 97-103

(1998).

[19] M. A. Contreras, K. Ramnathan, J. Abushama, F. Hasoon et al., Prog. Photovolt:

Res. Appl. 13, 209 (2005).

[20] R. Caballero, C. Gullien, Sol. Ene. Mat. Sol. cells 86, 1 (2005).

[21] F.O. Adurodija , M.J. Carter, R. Hill, Sol. Ene. Mat. and Sol. Cells 40, 359-369

(1996).

[22] B. Ullrich, H. Ezumi, S. Keitoku, T. Kobayashi, Mater. Sci. Eng. B 35, 117

(1995).

[23] G. Perna, V. Capaozzi, M. Ambrico, V. Augeli etal , Thin Solid Films 453, 187

(2004).

[24] S. R. Kodigala, A. K. Bhatnagar, R.D. Pilkington, A.E. Hill et al., J. Mater. Sci.

Mater. Electron. 11, 269 (2000).

[25] J. A. Hernandez, J.S. Hernandez, N.X. Quiebras et al., Sol. Ene. Mat. and Sol.

Cells 90, 2305 (2006).

[26] S. Spiering, L. Burkert, D. Hariskos, V.I. Roca et al., Thin Solid Films 359, 431-

432 (2003).

[27] P. Pistor, R. Caballero, D. Hariskos et al., Sol. Ene. Mat. Solar Cells 93, 148

(2009).

[28] S. Spiering, L. Burkert, D. Hariskos, M. Powalla et al., Thin Solid Films 517,

2328 (2009).

[29] S. H. Kwon, D.Y.Lee, B.T. Ahn, J. Korean Phys. Soc. 39, 655 (2001).

[30] M. Topic, F. Smole, J. Furlan, Sol. Ene. Mat. and Sol. Cells 49, 311 – 317 (1997).


100

[31] M. Burgelman, P. Nollet, S. Degrave, Thin Solid Films 361-362, 527-532 (2000).

[32] I. Hengel, A. Neisser, R. Klenk, M.L. Steiner, Thin Solid Films 361—362, 458-

462 (2000).

[33] T. Minemoto, T. Matsui, H. Takakura, Y. Hamakawa, T. Negami, Y. Hashimoto,

T. Uenoyama, M. Kitagawa, Sol. Ene. Mat. & Sol. Cells 67, 83-88 (2001).

[34] D.Y. Lee, B.T. Ahn, K.H. Yoon, J.S. Song, Sol. Ene. Mat. Sol. Cells 75, 73

(2003).

[35] A. Fahrenbruch, R.H. Bube, Fundamentals of solar cells, Academic Press, New

York, 1983.

Documents

CHAPTER-4 SIMULATION OF HETROJUNCTION SOLAR CELLS …shodhganga.inflibnet.ac.in/bitstream/10603/42510/8/09... · 2018-07-03 · CHAPTER-4 SIMULATION OF HETROJUNCTION ... semiconductor