Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.337
Publication date: 28th August 2024
Perovskite solar cells (PSCs) have recently achieved certified efficiencies of 26.7% for single-junctions and 33.9% for silicon-perovskite tandems, drawing significant attention from academia and industry. Spin coating remains the dominant fabrication method for high-efficiency PSC devices due to its simplicity and low cost. However, manual spin coating introduces variability, such as inconsistent solution dripping speed, pipette-to-substrate distance, and timing between steps. These issues, along with variations from researcher handovers, can affect device performance.
Robotic automation offers a solution to these challenges, ensuring reproducibility and minimizing human error. One key area for automation is the anti-solvent dripping step, crucial for inducing perovskite crystallization. This process is sensitive to variables like pipette position, angle, and speed, which manual methods struggle to control precisely.
In this study, we used the fully automated KMAP system (KRICT Multi-layer Automated Spin Coating System for Perovskite Devices) to finely control anti-solvent dripping. KMAP mimics human-like experimentation with a multi-joint robot arm, capable of handling 40 samples and 16 solutions per experiment, enabling high-throughput research.
We compared the effects of various anti-solvents (Toluene, Diethyl ether, Ethyl acetate, and Trifluorotoluene) and dripping speeds (5–25 mm/s). For low dielectric constant solvents like Toluene and Diethyl ether, slower dripping speeds yielded better results, while faster speeds worked best for high dielectric solvents like Trifluorotoluene. We also examined the impact of humidity, finding that Toluene and Diethyl ether were more sensitive to changes.
Through precise control and automation, KMAP allowed us to analyze the complex thin-film formation process, demonstrating the effectiveness of integrating robotics and AI to tackle experiments that are difficult for humans to perform manually.
This research was supported by the Digital Research Innovation Institution Program Through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT (RS-2023-00283597)
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