WHITE PAPER

Study on the Effect of the Firing Process to the Energy Conversion Efficiency of Solar Cells

Background

Over the last few years, solar cell manufacturers have strived to improve energy conversion efficiency at a lower cost. Optimizing the metal electrodes firing process in furnaces is a common way to achieve this goal. This was overlooked in the past. Most of the time, such thermal process work was done mainly by experienced engineers without much in-depth engineering study and development. This article will introduce a method of profiling and optimizing furnace setpoints in the Crystalline Silicon Solar Cell manufacturing process using SUNKIC, in order to achieve optimal results for a stable and high solar energy conversion at Topsola¡¯s factory in Suzhou, China.
Principle of Experiment

After selecting the initial furnace setpoints, the actual profile on the surface of the Crystalline Silicon solar cell was measured and recorded using KIC¡¯s SunKIC profiler. By methodically selecting new furnace setpoints and analyzing the resulting profile characteristics and their respective energy conversion efficiency, the process window and optimal profile can be determined using the profiler software. The energy conversion efficiency of each cell will be calculated.

Material List

Silicon solar cell: 125 x 125 mm
Aluminum paste: All sample use the same type of Aluminum paste
Silver paste: Front side Heraeus CL80-9235HL; Rear side Heraeus SOL230S

Experiment

A reference or base profile was selected to fire 30 pcs of solar cells. The average efficiencies were then computed. The final heating zone of the furnace was varied manually to develop different profiles. For each unique profile, 30 cells were fired . Table 1 shows the results of six different profiles. The 7th profile was determined by KIC¡¯s Spectrum optimization software. Figure 1 depicts the average cell efficiency for each set of 30 cells fired at the listed furnace setpoints. The actual peak measured temperature also is displayed.

Table 1: Average energy conversion efficiency of various profiles.

No	Avg. conversion efficiency %	Furnace setting ¡æ						Actual Peak temp ¡æ
		1	2	3	4	5	6
1	17.1276	400	500	610	700	800	895	820
2	17.1875	400	500	610	700	800	905	829
3	17.2766	400	500	610	700	800	920	842
4	17.2787	400	500	610	700	800	930	851
5	17.2778	400	500	610	700	800	945	865
6	17.1283	400	500	610	700	800	955	871
7*	17.2753	400	500	610	700	844	923	851
* Profile by our partner KIC's optimization software

Figure 1: Spectrum optimizing software interface

Analysis and Discussion The Box Chart shown in Figure 2 below depicts the results of the average energy conversion efficiency of solar cells fired at different profiles. We can see that result using profile 3, 4, 5 and 7 had a higher efficiency and less variation.. From Table 1, we can see that the average peak temperatures were 842¡ã~865oC. This is the peak temperature process window for firing such paste.

 

Figure 2: Average energy conversion efficiency for various profiles

Figure 3: Distribution chart of energy conversion efficiency for various profiles

The optimized profile 7 in Figure 3 has a 0.15 percent higher efficiency than the initial profiles . It also has better stability as compared to profiles 3, 4, 5 and 6. This reduces the number of lower efficiency cell produced, hence improving the productivity and cost at Topsola¡¯s factory in Suzhou, China.

Conclusion

In summary, the accurate measurment of the cell profiles and optimization of the furnace¡¯s temperature settings during production play an important role in process control. Frequent tracking on the furnace¡¯s temperature, collecting and analyzing measurement data can help optimize the firing process and find a suitable process window. The end result is the production of higher efficiency cells, stable production quality and reduced production cost.