Robotics and Machine Learning Optimize Perovskite Solar Cell Fabrication in Real Time
Researchers at the University of Osaka have developed an automated robotic platform integrated with machine learning to track perovskite film formation in real time and improve solar cell performance. In a study published in ACS Nano, the research team demonstrated an automated system that coordinates film deposition with high-resolution optical imaging and environmental measurements. The platform addresses a persistent challenge in solar cell manufacturing, where minor fluctuations in humidity, temperature, and related processing conditions during liquid crystallization can substantially alter film quality and electrical efficiency.
Perovskite solar cells generate electricity by absorbing sunlight in a thin light-absorbing layer formed from a liquid precursor solution that dries and crystallizes into a solid film. To understand how early processing conditions dictate final material quality, the investigators tracked the transformation from liquid coating to crystalline film, focusing specifically on rapid changes in film color and the behavior of antisolvent droplets. Because these transitions occur on very short timescales, the automated platform was designed to capture color variations at subsecond intervals while resolving the motion and impact of antisolvent droplets within a few milliseconds.
The high-speed observations enabled the team to evaluate how the precise timing and physical behavior of antisolvent droplets govern subsequent film crystallization. Lead author Ryosuke Kado explained that the project aimed to move beyond conventional trial and error by measuring physical changes as the film actively forms. According to Kado, the system allows researchers to directly connect real-time physical shifts with the resulting quality of the perovskite film and isolate the operational factors that matter most.
The addition of machine learning algorithms allowed the researchers to interpret the large volumes of diagnostic data produced by the robotic system. Senior author Akinori Saeki noted that automated instrumentation collects significantly richer information than is practical through manual fabrication methods. Saeki explained that machine learning aids in parsing these multifaceted variables, revealing relationships between processing conditions and material outcomes that are otherwise difficult to detect.
The researchers applied findings from the machine learning analysis to refine the antisolvent coating process, determining an optimal range for droplet behavior. Using these optimized parameters, the team fabricated a lead-based perovskite solar cell device that achieved a power conversion efficiency of 24.4 percent. The experimental result demonstrated that data collected during the transient stages of film formation can be systematically utilized to enhance the performance of finished photovoltaic hardware.
By unifying automated experimentation, real-time measurements, and statistical data analysis, the platform provides a systematic protocol for uncovering previously unknown relationships between fabrication variables and thin-film quality. The researchers reported that this automated approach could help catalyze ongoing photovoltaic research and support the development of more practical perovskite solar cells.
Subject of Research: Automated fabrication and machine learning analysis of perovskite solar cells
Article Title: Bright spark: Robots and AI track solar cell fabrication
Article References: Bright spark: Robots and AI track solar cell fabrication. Original publication (Bibliographic details unavailable.)
Image Credits: AI Generated
DOI: Not provided
Keywords: perovskite solar cells, machine learning, automated fabrication, photovoltaics, materials science, University of Osaka, solar energy, thin films, Bright, spark, Robots, track
News Source: Teresa Odom. (November 12, 2026). Robotics and Machine Learning Optimize Perovskite Solar Cell Fabrication in Real Time. Scienmag.
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