Proceedings of Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics (IPEROP27)
Publication date: 22nd October 2026
The reproducibility of perovskite thin-film fabrication remains a critical challenge in laboratory-scale research, particularly in spin-coating processes involving manually controlled antisolvent dispensing. Variations in operator-dependent parameters, such as dispensing timing, pipette tip height, and dispensing rate, can significantly influence perovskite crystallization and film quality. These inconsistencies not only affect experimental reliability but also limit systematic material screening and the development of reliable datasets for data-driven research. In this study, a robotic-arm-assisted spin-coating platform was developed to minimize human-induced variations and provide precise control over antisolvent dispensing conditions. The variability of manually operated spin coating was evaluated by comparing three operators with robotic dispensing. Preliminary measurements revealed substantial trial-to-trial variations in the manually fabricated samples, whereas robotic dispensing exhibited more consistent optoelectronic properties. Furthermore, the effects of key dispensing parameters, including the gear-driven dispensing interval setting, dispensing timing, and pipette tip height, were systematically investigated using quasi-Fermi level splitting (QFLS) measurements. The experimental results demonstrated that antisolvent dispensing conditions strongly influenced both the average implied open-circuit voltage (iVoc) and its spatial variation. Among the investigated conditions, dispensing at 3 seconds before the end of spin coating achieved the highest mean iVoc of 1.2420 V, with a standard deviation of only 1.79 mV. Compared with the reference condition, this represented an improvement of 5.7 mV in mean iVoc and a 76.5% reduction in the standard deviation of the measured values. These findings demonstrate the potential of robotic antisolvent dispensing to improve process control and thin-film uniformity. The established platform provides a foundation for further design-of-experiments (DOE) optimization, reproducible material screening, and the development of reliable experimental datasets for future artificial intelligence-assisted perovskite solar cell research.
We thank the Energy Administration, Ministry of Economic Affairs, Taiwan (R.O.C.) (Grant No. 116-S0102) for funding this study.
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