4. Brief on the process design

diffraction (LEED), angular resolved ultraviolet photoelectron spectroscopy (ARUPS) and

Sample Figure 7(a) and (b) were obtained from the different origins, note that the differ-

4. Secondary ion mass spectrometry (SIMS) depth profile of Cu, In, Ga, Se, Cd, and Na

ence in the Ga profile in the SIMS depth profiles are due to the different processes.

Figure 7. SIMS depth profile of Cu, In, Ga, Se, Cd, and Na. (1) CdS/CIGS and ZnO diffusion. (2) Cu, In, Ga, and Se depth

revealed the CdS/CIGS/ZnO diffusion phenomena as shown in Figure 7 [19].

auger electron spectroscopy (AES) [14].

136 Emerging Solar Energy Materials

Figure 6. The complete band alignment includes the copper depletion at the interface.

5. Defect in grains and grain boundaries [20].

profile. (3) Quantitative analysis of Na in CIGS.

In sections above, the opto-electrical properties of the polycrystalline semiconductors are affected by structure and composition greatly.

In this section, the way of the poly-structure generation is better described through careful observation of the transfer procedures during the manufacturing process, which would be beneficial to know the deposited atoms final positions and the microstructures of the materials. The composition and the structures could be tuned based on controlling and modifying the process by adjusting the process parameters in order to obtain the desired opto-electrical properties.

We describe the better understanding of the way to generate poly-structure through careful observation of the particle transfer procedures during the manufacturing processes after knowing the final positions of the deposited atoms and the materials microstructures. On controlling and modifying the process, the composition as well structure can be tuned directly by varying the process parameters to acquire the desired opto-electrical properties.

The metallic grain structures are described by the original Thornton's zone model [21] according to the sputtering gas pressure and the substrate temperature. Since the high deposition rate, the magnetron sputtering process is most preferred for the industrial application. Also, the energy-dependent sputter yield is noted. Ellmer illustrates the inter-relationships between the process parameters (like substrate temperature and deposition rate) and the structural/optoelectrical properties in a new model [22]. However, the pressure (particles momentum) effect is not considered in this model.

### 4.1. Berg's model

The Berg's model is particularly utilized in the ZnO reactive sputtering. Basically, the surface coverage on the deposited film is just the composition of the film, but the composition is not easy to control since the system is unstable, where it requires the plasma diagnose sensor for feedback control. About the basic idea of the Berg's model [23, 24], the changes of the number of absorbed oxygen atoms per unit area Nx at the surfaces of the target is:

$$\frac{\text{dN}\_{\text{T}}}{\text{dt}} = 2\alpha\_{\text{t}}(1-\theta\_{\text{1}}) - \text{J/eS}\_{\text{N}}\theta\_{\text{1}} \tag{6}$$

At the substrate:

$$\frac{d\mathbf{dN}\_S}{d\mathbf{t}} = 2a\_\mathbb{C} \mathbf{F}(1-\theta\_2) + \frac{I}{e} \mathbf{S}\_N \theta\_1 \frac{A\_t}{A\_\mathbb{C}} (1-\theta\_2) - \frac{I}{e} \mathbf{S}\_M (1-\theta\_1) \frac{A\_t}{A\_\mathbb{C}} \theta\_2 \tag{7}$$

where αx is the sticking coefficients, Sx is sputtering yields, and θX is the coverage. What shown in Figure 8 is the basic idea of Berg's model and in Figure 9 is an example of the simulation result of the ZnO reactive sputtering system (θ<sup>2</sup> always <1). We also use the Berg's model to predict the composition of the compound thin-film. After that, we can also predict whether the operating point is stable. Therefore, we can investigate the time-dependent behavior of the reactive sputtering (Figure 10).

#### 4.2. CISe RTP-the IEC's model

$$\text{Cu}\_{\text{x}}\text{In}\_{\text{y}} + \text{H}\_{2}\text{Se} \text{ (or } \text{Se)}.\tag{8}$$

For Cu In.

where Ki <sup>¼</sup> Kioexp �Eai

independent constant.

RT � �

Figure 9. A simulation result of the ZnO reactive sputtering system [25].

2In þ Se ! In2Se k2 (9)

In2Se þ Se ! 2InSe k3 (10)

2CuIn þ 2Se ! Cu2Se þ In2Se ka (11)

2InSe þ Cu2Se þ Se ! 2CuInSe2 k7 (12)

and V=PnixNAvo xVunitcell, nunitcell, is the film volume taken as a time-

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ni ¼ ½ �i xV are the mole numbers, nunitcell is the number of pairs of the atoms in the unit cells. Figure 10 shows an example of the Se or H2Se RTP of Cu:In:Se = 0.24:0.25:0.51 CuxIny films at 450�C, μCu = 0, μIn = 0; the mole ratio of the constitute atoms (in the film) as functions of the processing time. (Film volume = (2.5 cm) 2 � 2 μm). In our non-stoichiometric case, the initial mole concentrations of Cu and in taken from our calculated data. Then, these were input to the CuxIny and in mole concentrations in order to solve the ordinary differential equation system. Regarding building the non-stoichiometric structures, we build the XRD spectrum. The supercell values have been used to calculate the XRD spectrum of non-stoichiometric CuInSe2

After we finish the CuInSe2 defect concentration calculations, we can apply the result to the model, which was developed by IEC [26] to predict the processing time under a certain process temperature.

Figure 8. The basic idea of the Berg's model.

Figure 9. A simulation result of the ZnO reactive sputtering system [25].

For Cu In.

dNT

J e SNθ<sup>1</sup> At AC

dt <sup>¼</sup> <sup>2</sup>αCF 1ð Þþ � <sup>θ</sup><sup>2</sup>

At the substrate:

138 Emerging Solar Energy Materials

dNS

ior of the reactive sputtering (Figure 10).

4.2. CISe RTP-the IEC's model

Figure 8. The basic idea of the Berg's model.

temperature.

dt <sup>¼</sup> <sup>2</sup>αtð Þ� <sup>1</sup> � <sup>θ</sup><sup>1</sup> <sup>J</sup>=eSNθ<sup>1</sup> (6)

SMð Þ 1 � θ<sup>1</sup>

CuxIny þ H2Se or Se ð Þ: (8)

At AC

θ<sup>2</sup> (7)

J e

ð Þ� 1 � θ<sup>2</sup>

where αx is the sticking coefficients, Sx is sputtering yields, and θX is the coverage. What shown in Figure 8 is the basic idea of Berg's model and in Figure 9 is an example of the simulation result of the ZnO reactive sputtering system (θ<sup>2</sup> always <1). We also use the Berg's model to predict the composition of the compound thin-film. After that, we can also predict whether the operating point is stable. Therefore, we can investigate the time-dependent behav-

After we finish the CuInSe2 defect concentration calculations, we can apply the result to the model, which was developed by IEC [26] to predict the processing time under a certain process

$$\text{2In} + \text{Se} \to \text{In}\_2\text{Se} \,\text{k}\_2 \tag{9}$$

$$\text{In}\_2\text{Se} + \text{Se} \to 2\text{InSe}\,\text{k}\_3\tag{10}$$

$$2\text{CuIn} + 2\text{Se} \rightarrow \text{Cu}\_2\text{Se} + \text{In}\_2\text{Se k}\_4 \tag{11}$$

$$2\text{InSe} + \text{Cu}\_2\text{Se} + \text{Se} \to 2\text{CuInSe}\_2\text{ k}\_7\tag{12}$$

where Ki <sup>¼</sup> Kioexp �Eai RT � � and V=PnixNAvo xVunitcell, nunitcell, is the film volume taken as a timeindependent constant.

ni ¼ ½ �i xV are the mole numbers, nunitcell is the number of pairs of the atoms in the unit cells. Figure 10 shows an example of the Se or H2Se RTP of Cu:In:Se = 0.24:0.25:0.51 CuxIny films at 450�C, μCu = 0, μIn = 0; the mole ratio of the constitute atoms (in the film) as functions of the processing time. (Film volume = (2.5 cm) 2 � 2 μm). In our non-stoichiometric case, the initial mole concentrations of Cu and in taken from our calculated data. Then, these were input to the CuxIny and in mole concentrations in order to solve the ordinary differential equation system.

Regarding building the non-stoichiometric structures, we build the XRD spectrum. The supercell values have been used to calculate the XRD spectrum of non-stoichiometric CuInSe2

Figure 10. The Se or H2Se RTP of Cu:In:Se = 0.24:0.25:0.51 CuxIny films at 450C, μCu = 0, μIn = 0; the mole ratio of the constitute atoms (in the film) as functions of the processing time (120 min). (Film volume = (2.5 cm) 2 2 μm).

Figure 12. Preliminary result on the metal organic sputtering of CIGS PV. (a) Ga content as a function of TMGa flow rate, (b) the content of Ga as a function of substrate temperature, (c) plain view SEM image of the deposited CIGS film, and (d)

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Figure 13. Room temperature Raman shift in CuIn1–xGaxSe2 thin-films of thickness 600 nm deposited on a glass substrate with a change in Ga content, the Raman peak shifted from 173.8 to 184.6 cm<sup>1</sup> in A1 mode and u is Se shift

cross-section.

parameter. Reproduced with permission.

Figure 11. Simulated XRD spectra of non-stoichiometric CuInSe2; Cu:In:Se2 = 0.21:0.26:0.53.

and CuGaSe2. In our work, the method described by Attia et al. [27] has been incorporated and only modify the structural factor by summing over all the atoms in the supercell. The defect site in each defect cells has been chosen randomly. We observed the presence of extra small peaks in the XRD spectrum, as shown in Figure 11 [14].

Anomalous neutron diffraction scattering of synchrotron X-ray radiations gives more accurate data of composition distributions [28]. However, in-situ XRD is the most convenient tool for monitoring the deviations from the stoichiometric compositions. An incapability gap is indicated by the substantial increase in full width at half maximum (FWHM), in which the lattice constants depends on Ga/III and follow the Vegrad's law [29–31]. For industrial use for future process monitoring and control, more work should be done to make it more feasible to be used.

#### 5. Some means to improve film composition control

When CIGS or other thin-films were fabricated, there will be chemical fluctuation-induced nano-domains. We developed a novel metal-organic sputtering (MO-sputtering) technique in our work, in which metal organic compound such as trimethygallium (TMGa) as the reactant was used during the reactive sputtering procedure. In Figure 12, the Ga/(Ga + In) ratio changes Some Essential Issues and Outlook for Industrialization of Cu-III-VI2 Thin-Film Solar Cells http://dx.doi.org/10.5772/intechopen.77023 141

Figure 12. Preliminary result on the metal organic sputtering of CIGS PV. (a) Ga content as a function of TMGa flow rate, (b) the content of Ga as a function of substrate temperature, (c) plain view SEM image of the deposited CIGS film, and (d) cross-section.

and CuGaSe2. In our work, the method described by Attia et al. [27] has been incorporated and only modify the structural factor by summing over all the atoms in the supercell. The defect site in each defect cells has been chosen randomly. We observed the presence of extra small

Figure 10. The Se or H2Se RTP of Cu:In:Se = 0.24:0.25:0.51 CuxIny films at 450C, μCu = 0, μIn = 0; the mole ratio of the

constitute atoms (in the film) as functions of the processing time (120 min). (Film volume = (2.5 cm) 2 2 μm).

Anomalous neutron diffraction scattering of synchrotron X-ray radiations gives more accurate data of composition distributions [28]. However, in-situ XRD is the most convenient tool for monitoring the deviations from the stoichiometric compositions. An incapability gap is indicated by the substantial increase in full width at half maximum (FWHM), in which the lattice constants depends on Ga/III and follow the Vegrad's law [29–31]. For industrial use for future process

When CIGS or other thin-films were fabricated, there will be chemical fluctuation-induced nano-domains. We developed a novel metal-organic sputtering (MO-sputtering) technique in our work, in which metal organic compound such as trimethygallium (TMGa) as the reactant was used during the reactive sputtering procedure. In Figure 12, the Ga/(Ga + In) ratio changes

monitoring and control, more work should be done to make it more feasible to be used.

peaks in the XRD spectrum, as shown in Figure 11 [14].

140 Emerging Solar Energy Materials

Figure 11. Simulated XRD spectra of non-stoichiometric CuInSe2; Cu:In:Se2 = 0.21:0.26:0.53.

5. Some means to improve film composition control

Figure 13. Room temperature Raman shift in CuIn1–xGaxSe2 thin-films of thickness 600 nm deposited on a glass substrate with a change in Ga content, the Raman peak shifted from 173.8 to 184.6 cm<sup>1</sup> in A1 mode and u is Se shift parameter. Reproduced with permission.

Figure 14. Micro-Raman spectroscopy results on the composition of thin-films.

as a function of TMGa flow rate and substrate temperature [1]. And the linear relationship of Ga/(Ga + In) with the TMGa flow rate to adjust the deposited film composition is particularly interesting. I could conclude from in Figure 12 that the Raman shift as a function of the film composition change of Cu/(Ga + In) from the micro-Raman spectroscopy. It is clear that the micro-Raman shift is sensitive to the composition change of the CIGS thin-films. If we combine the use of Mo-Sputtering for the film growth and its feedback monitoring with the Raman shift, a means to better control the stoichiometry of thin-film might be provided during the manufacturing steps (Figures 13 and 14).
