With silicon solar cells as the research object, this paper takes their nondestructive character and analyzes their quality with noise-based technology. Through the stress aging of samples and the testing of various types of noise, including 1/f noise, microplasma noise, and G-R noise, in the cells in the laboratory, the noise characteristics are analyzed. The research results show that noise is correlated with defects in silicon solar cells. The different types of noise can be used to characterize different parts and types of defects in the cells according to the mechanism of generation of noise and the failure physics of photovoltaic devices, and thereby, the evaluation of the quality and analysis of the reliability of solar cells can be achieved.

In recent years, photovoltaic power generation technology has been developing rapidly, and certain deep-seated issues restricting development in the photovoltaic industry have stood out. First, the photoelectric conversion efficiency has increased very slowly for the first and second generations of cells supported by relatively mature technology; second, the rapid expansion of industries has caused the productive capacity to significantly lag. Hence, it is necessary for photovoltaic products to undergo quality certification to accelerate the upgrading of obsolescent technology. Finally, for the type selection of enterprise products, it is imperative to set a corresponding quality evaluation standard to extend the life expectancy of photovoltaic systems and ameliorate the economic income. All the above-mentioned issues require a proposal of quality tests and reliability analyses applicable to photovoltaic products.

There are some deficiencies in the traditional quality test technology and reliability analysis for solar cells. Surface defects of solar cells, such as unfilled corners, breakages, cracks, and broken gates, may be discovered through visual inspection, but the judgment process is susceptible to subjective factors; therefore, it is difficult to set up uniform criteria. Acoustic detection can be carried out by selecting interface regions at different depths within the cell sheet or panel to reflect the conditions of stratification of adhesive and poor contact between the air hole and metal board but is not sufficiently sensitive to the defects inside a single material. Although electroluminescent detection can reflect the number and diffusion length of minority carriers inside a cell, this detection method is destructive and does not quantitatively characterize defects well. With the electric parameter detection method most commonly adopted in national or industrial standards, the parameters are easy to obtain; however, the sensitivity of the characterization is not high, and no direct relations with defects are available. Therefore, a more rapid, sensitive, nondestructive technology directly related to defects is needed to test and characterize the quality of photovoltaic products.1–7 

Noise originates from the stochastic fluctuations of physical quantities. In recent years, noise has been treated as a tool for the evaluation of the quality and reliability of electronic components both at home and abroad. Considering their effectiveness, sensitivity, speediness, nondestructiveness, and capability of being connected directly to the microscopic and medium-scale defects in materials, noise-based methods have been widely applied in the characterization and analysis of the reliability of various semiconducting materials and devices.8–16 The earliest foreign researchers started to apply electric noise in the characterization of the quality of solar cells in the 1980s. Over the recent years, it has been successively found that there exist different types of noise in solar cells. Robert et al. tested the positively biased noise in solar cells, finding that 1/f noise mainly originates from the diffusion mechanism and recombination mechanism of minority carriers and proposed the corresponding quantitative model of this 1/f noise.17,18 Robert’s research group first discovered microplasma noise in reversely biased p-n junctions, the appearance of which illustrates that microdefect clusters that cause the breakdown of microplasmas exist in p-n junctions.19 Chobola studied the noise characteristics of monocrystalline solar cells and found that G-R noise exists in some of the cells, which he thought stemmed from the dislocations in the photovoltaic materials, and impurities at deep energy levels can result in the enhancement of this noise.20 

The aforementioned research shows that the noise in solar cells mainly manifests as 1/f noise, microplasma noise, and G-R noise. According to the mechanism of the generation of the different types of noise, combined with the physics of failure of solar cells, noise can be connected with different defects at different positions of cells.21 The physical mechanism of the generation of 1/f noise involves fluctuations in the number or mobility rate of carriers during the carrier transportation process. In solar cells, 1/f noise stems mainly from defects on the surface or interface, such as incomplete lattices, unsaturated dangling bonds, or transmission center capture. Microplasma noise reflects from the metal impurities and microdefect clusters in the area of electric charges in the p–n junction space. G-R noise arises mainly from the scattering effect of impurities at deep energy levels inside semiconducting materials on carriers, reflecting defects such as heavy metal contamination precipitation, dislocations, or split levels in the base region or emission region materials. Therefore, based on the characteristics of the above-mentioned types of noise and their connection with different positions and defects in solar cells, the quality of solar cells can be characterized by noise testing. Figure 1 exhibits the interrelationship among noise, defects, and processes, with silicon-based cells as an example.

FIG. 1.

Relationships between noise, defects, and technology in silicon solar cells.

FIG. 1.

Relationships between noise, defects, and technology in silicon solar cells.

Close modal

To study the effect of noise characterization on the reliability of solar cells, samples of monocrystalline silicon solar cells were selected to undergo high-temperature stress and radiation stress tests for actual measurement of the electric parameters and noise parameters of the samples for comparison and analysis. The test samples were monocrystalline silicon cells 2DU3. In the high-temperature stress test, the samples were tested once every 8 h to obtain the magnitude of stress at a constant temperature of 120 °C; in the radiation stress test, the dose rate of the ionization source was 50 rad/s. Both groups of stress tests were conducted at room temperature. The specific test details are as follows:

The whole test system should meet the following requirements:

  1. The solar cell is vulnerable to external interference due to its weak noise signal. In order to prevent interference and ensure the accuracy of test results, the whole experimental testing process is carried out in a shielding room.

  2. The battery to be tested is sensitive to static electricity and can be easily damaged, so when replacing the device to be tested, it is necessary to prevent electrostatic interference and pay attention to scratches.

  3. In order to accurately test the electrical parameters of the battery to be tested, the local noise of the amplifier is required to be low to improve the accuracy of the test.

  4. Before testing the battery’s electrical parameters, the solar simulator should be turned on in advance to preheat so as to ensure that the simulated sunlight can be emitted smoothly.

According to the basic requirements of the electrical test system described in detail above, the electrical test system of the solar cell in this experiment is designed. The system framework of the basic test is shown in Fig. 2. The detailed steps are as follows:

FIG. 2.

Solar cell test system.

FIG. 2.

Solar cell test system.

Close modal

Step (1): Before the experimental test, turn on the sunlight simulator for preheating in advance to ensure emission of stable sunlight so as to reduce the experimental error.

Step (2): Put the experimental sample into the semiconductor tester and close the box cover to avoid sunlight into the sample so as to simulate the electrical characteristics of the battery under dark conditions.

Step (3): Open the sample box, and place the battery under the light emitted by the sunlight simulator, the light surface of the battery to be tested is perpendicular to the light emitted by the simulator; then, test the electrical characteristics of the battery to be tested.

Step (4): Replace the sample, and repeat steps (2) and (3).

According to the test system of the solar cell, the noise test scheme is developed, as shown in Fig. 3. The test results include the variation in electrical parameters in the high temperature stress experiment and the variation in noise parameters in the high temperature stress experiment.

FIG. 3.

Solar cell noise test flow chart.

FIG. 3.

Solar cell noise test flow chart.

Close modal

First, research was conducted on the initial noise in the samples that underwent the same process. Three slices of samples were taken. Figure 4 shows the I–V characteristic curves of the three monocrystalline silicon solar cells. Figure 4 reveals the output capacities of the three cells, denoted Nos. 1–3, from the strongest to the weakest. Their maximum output powers are extracted separately, and it is found that P1 = 3.38 × 10−6 W, P2 = 3.11 × 10−6 W, and P3 = 2.58 × 10−6 W. Evidently, despite tiny differences among the three, the 1/f noise spectra of the three cells are significantly different, as shown in Fig. 5. With this as the background noise from the bottom to the top in Fig. 5, the maximum difference in the magnitudes of sample Nos. 1–3 is almost 2 orders of magnitude on the low-frequency end, which is focused upon. The noise of cell No. 1 is the lowest, meaning its concentration of internal defects is the lowest, while the noise of cell No. 3 is the highest, meaning it exhibits the worst quality.

FIG. 4.

I–V curves of three solar cells under illumination.

FIG. 4.

I–V curves of three solar cells under illumination.

Close modal
FIG. 5.

1/f noise characteristics of the solar cells.

FIG. 5.

1/f noise characteristics of the solar cells.

Close modal

In the high-temperature aging test, the degradation of the noise parameters of the samples is more sensitive than the degradation of the regular electric parameters. One of the up-to-standard samples was selected arbitrarily to undergo an aging test lasting 72 h and denoted No. 4. The conversion efficiency of this sample is 21.52% before the test, while the efficiency degrades to 17.85% after 72 h, and the relative change is 19.11%; the noise test results under the same conditions include an amplitude of 1.93 × 10−14 V2/Hz of 1 Hz noise before aging, which becomes 6.57 × 10−11 V2/Hz after 72 h, with a relative change of 5.58 × 105%. 1/f noise mainly originates from defects on the device surface or the interface, so noise parameters are more suitable for characterizing the quality of solar cells.

In the reversely biased tests of samples, individual samples are found to exhibit microplasma noise after high-temperature aging. We take up-to-standard sample No. 5 as an example. With a gradual increase in the reversely biased voltage, the power spectral density of the noise increases gradually. When it reaches 30 V during the test, a platform appears on the low-frequency end in the frequency domain, as shown in Fig. 6. The pulse noise measures 50 µA in magnitude, the width of the pulse ranges from 5 to 850 µs, and the amplitude is 10−17 in the frequency domain, all of which are coincident with the results of the foreign test. This indicates that solar cell No. 5 has suffered a partial avalanche breakdown under the reversely biased state of 30 V and that the microdefect clusters near the p-n junction result in the occurrence of microplasma noise.

FIG. 6.

Plasma noise in inverse solar cells.

FIG. 6.

Plasma noise in inverse solar cells.

Close modal

Figure 7 shows the phenogram of the G-R noise in the solar cells. Two up-to-standard samples were taken arbitrarily and denoted Nos. 6 and 7. Figure 7 reveals that there is a slight difference between the electric parameters of cell No. 7 and cell No. 6, which is manifested as the difference in the equivalent series and parallel resistances, while the noise spectrum in Fig. 7 conspicuously reflects that the noise spectrum of cell No. 7 exceeds that of cell No. 6 by two orders of magnitude. More importantly, marked G-R noise appears for cell No. 7 at 1 kHz, meaning that the major cause of its low quality is associated with heavy metal precipitation contamination arising in the cell.1 That the characteristics of the noise are directly related to defects is more conducive to the in-depth analysis and evaluation of the quality of cells.

FIG. 7.

G-R noise characterization of solar cells.

FIG. 7.

G-R noise characterization of solar cells.

Close modal

The conversion efficiency is related to the structure and defects of solar cells. Due to the existence of defects, the compound current component in the solar cell is increased, and the conversion efficiency is lower than the ideal value. Therefore, the quality of solar cells can be evaluated by comparing the initial noise (SV0) between different devices. Figure 8 shows the relationship between conversion efficiency and initial noise. It can be seen from Fig. 8 that the lower the noise, the higher the conversion efficiency.

FIG. 8.

Relationship between conversion efficiency and initial noise.

FIG. 8.

Relationship between conversion efficiency and initial noise.

Close modal

Impurities and defects in solar cells are not only introduced from raw materials and technical processes but also by the practical use process of cell products, thereby causing a decline in the performance of cells due to the effect of stresses in the external environment, such as spatial radiation, high-temperature stress, and alternating stress at high and low temperatures. To characterize the reliability of solar cells in application, a batch (15 slices) of silicon solar cells were selected to undergo ionizing radiation; the frequency of the extracted noise spectrum value is 600 Hz.

Figure 9 shows the relation between the initial amplitude of noise in the cell before radiation and the amplitude of the noise after radiation. The horizontal axis denotes the initial amplitude of noise before radiation (0 krad), whereas the vertical axis denotes the amplitude of noise (Sv) after radiation (600 krad). Figure 9 reveals that the lower the initial noise in the cell that has not experienced stress damage is, the lower the amplitude of noise after radiation will be. This explains why the performance of samples with lower initial noise degrades less because of environmental stresses. This result shows that the initial noise can be used to predict the performance degradation of a sample in an operating environment. Figure 10 shows the relationship between the noise power spectral density and the relative variance rate (SV%) after radiation damage. It can be seen from Fig. 10 that the magnitude of the noise power spectral density after radiation damage is determined by the magnitude of the noise change rate after the initial radiation damage. This bears high similarity to the reliability predictions with noise in MOSFETs,22 a perfect accomplishment of the nondestructive characterization of solar cells.

FIG. 9.

Relationship between the initial noise and radiation noise of solar cells.

FIG. 9.

Relationship between the initial noise and radiation noise of solar cells.

Close modal
FIG. 10.

Relationship between the relative variance rate and radiation noise of solar cells.

FIG. 10.

Relationship between the relative variance rate and radiation noise of solar cells.

Close modal

This paper conducted an aging test of samples under stress, detected various types of noise, including 1/f noise, microplasma noise, and G-R noise, in the cells, and analyzed their characteristics. The research suggests that noise can play an active role in the characterization of the quality of solar cells and in the analysis of their reliability. This method of characterization is superior to others due to its sensitivity and nondestructiveness. In addition, noise can distinguish defects of different types at different parts of the cell, which is conducive to improving the production process and guiding the type selection of products. Finally, noise can also function as a monitor of the reliability of some components and the encapsulation in a cell panel.

This research was supported by the National Natural Science Foundation of China (Grant No. 61801005), the Outstanding Young Talents Project in Shaanxi Province (Grant No. 2018JCRC01), the Scientific Research Fund of Shaanxi Provincial Education Department (Grant No. 19JK0012), and the Science and Technology in Ankang Project (Grant No. AK2019GY-07).

The data that support the findings of this study are available from the corresponding author upon reasonable request.

1.
Z.
Chobola
and
A.
Ibrahim
, “
Noise and scanning by local illumination as reliability estimation for silicon solar cells
,”
Fluctuation Noise Lett.
1
(
1
),
L21
L26
(
2001
).
2.
Z.
Chobola
, “
Noise as a tool for non-destructive testing of single-crystal silicon solar cells
,”
Microelectron. Reliab.
41
,
1947
1952
(
2001
).
3.
Z.
Chobola
, “
Measurement of low frepuency noise of monocrystalline silicon solar cells
,” in
XVII IMEKO World Congress Metrology in the 3rd Millenniumb
(
IMEKO World Congress
,
Dubrovnik, Croatia
,
2003
), pp.
783
785
.
4.
P. V. V.
Jayaweera
, “
1/f noise and dye-sensitized solar cells
,”
Semicond. Sci. Technol.
20
,
L40
L43
(
2005
).
5.
P. V. V.
Jayaweera
,
P. K. D. D. P.
Pitigala
,
M. K. I.
Seneviratne
,
A. G. U.
Perera
, and
K.
Tennakone
, “
1/f noise in dye-sensitized solar cells and NIR proton detectors
,”
Infrared Phys. Technol.
50
,
270
273
(
2007
).
6.
S.
Varun
,
H. R.
Sathy
,
B.
Anitha
 et al, “
A comparative study of defect density of states for single, mixed and bulk heterojunction perovskite solar cells
,”
AIP Conf. Proc.
2082
,
050013
(
2019
).
7.
G.
Landi
,
C.
Barone
,
C.
Mauro
 et al, “
Noise spectroscopy as a tool for the characterization of perovskite, organic and silicon solar cells
,”
AIP Conf. Proc.
2082
,
020001
(
2019
).
8.
L.
Skvarenina
and
R.
Macku
, “
Noise and optical spectroscopy of single junction silicon solar cell
,”
Metrol. Meas. Syst.
25
(
2
),
303
316
(
2018
).
9.
L. N.
Hu
,
L.
He
,
H.
Chen
 et al, “
Defect characterization of amorphous silicon thin film solar cell based on low frequency noise
,”
Sci. China Inf. Sci.
61
(
6
),
069403
(
2018
).
10.
A.
Singh
,
P. K.
Nayak
,
S.
Banerjee
 et al, “
Insights into the microscopic and degradation processes in hybrid perovskite solar cells using noise spectroscopy
,”
Solar RRL
2
(
1
),
1700173
(
2017
).
11.
L. K. J.
Vandamme
, “
Noise as a diagnostic tool for quality and reliability of electronic devices
,”
IEEE Trans. Electron Devices
41
(
11
),
2176
2187
(
1994
).
12.
M. M.
Jevtic
, “
Noise as a diagnostic and prediction tool in reliability physics
,”
Microelectron. Reliab.
35
(
3
),
455
477
(
1995
).
13.
C.
Ciofi
and
B.
Neri
, “
Low-frequency noise measurements as a characterization tool for degradation phenomena in solid-state devices
,”
J. Phys. D: Appl. Phys.
33
(
21
),
R199
R216
(
2000
).
14.
B. K.
Jones
, “
Electrical noise as a reliability indicator in electronic devices and components
,”
Circuits Devices Syst.
149
(
2
),
13
22
(
2002
).
15.
J.
Smulko
,
A.
Azens
,
R.
Marsal
 et al, “
Application of 1/f current noise for quality and age monitoring of electrochromic devices
,”
Sol. Energy Mater. Sol. Cells
92
(
8
),
914
918
(
2008
).
16.
J.
Mojtaba
and
S.
Majid
, “
Investigation of the tensile strain influence on flicker noise of organic solar cells under dark condition
,”
Org. Electron.
59
,
230
235
(
2018
).
17.
L. K. J.
Vandamme
,
R.
Alabedra
, and
M.
Zommiti
, “
1/f noise as a reliability estimation for solar cells
,”
Solid-State Electron.
26
(
7
),
671
674
(
1983
).
18.
M.
Robert
and
P.
Koktavy
, “
Analysis of fluctuation processes in forward-biased solar cells using noise spectroscopy
,”
Phys. Status Solidi A
207
(
10
),
2387
2394
(
2010
).
19.
M.
Robert
and
P.
Kokatvy
, “
Study of solar cells defects via noise measurement
,” in
31st International Spring Seminar on Electronics Technology
(
IEEE
,
Budapest, Hungary
,
2008
).
20.
Z.
Chobola
, “
Impulse noise in silicon solar cells
,”
Microelectron. J.
32
(
9
),
707
711
(
2001
).
21.
G.
Landi
,
C.
Barone
,
C.
Mauro
,
H. C.
Neitzert
, and
S.
Pagano
, “
A noise model for the evaluation of defect states in solar cells
,”
Sci. Rep.
6
,
29685
(
2016
).
22.
D. M.
Fleetwood
,
M. R.
Shaneyfelt
, and
J. R.
Schwank
, “
Estimating oxide-trap, inter-trap, and border-trap charge densities in metal-oxide-semiconductor transistors
,”
Appl. Phys. Lett.
64
(
15
),
1965
1968
(
1994
).