Affiliation:
1. College of Integrated Circuits and Optoelectronic Chips Shenzhen Technology University Shenzhen China 518118
2. College of Engineering Physics Shenzhen Technology University Shenzhen China 518118
3. School of Materials Science and Engineering Hubei University Wuhan China 430062
4. School of New Materials and New Energies Shenzhen Technology University Shenzhen China 518118
Abstract
This work adopts machine learning to refine the annealing process in solution‐processed thin films, such as QDs, perovskites, and organic semiconductors. The annealing temperature and time of PbS QD thin film is optimized using a machine learning algorithm. This method offers a systematic approach to determine the optimal annealing parameters, surpassing the traditional, less‐efficient methods. This adaptive model minimizes human bias, handles complex nonlinear relationships, and capable of optimizing multiple parameters simultaneously. By introducing machine learning algorithm, this method provides a universal and effective strategy for the optimization of parameters during the fabrication of thin films.
Reference8 articles.
1. Stress- induced phase-alteration in solution processed indium selenide thin films during annealing;Mondal BK;RSC Adv,2021
2. Solution-Processed BiI3 Thin Films for Photovoltaic Applications: Improved Carrier Collection via Solvent Annealing
3. Synthesis of conjugated microporous polymer-based fluorescent “turn-off” sensor for selective detection of picric acid
4. Wearable 1 V operating thin-film transistors with solution- processed metal-oxide semiconductor and dielectric films fabricated by deep ultra-violet photo annealing at low temperature;Yu BS;Sci Rep,2019
5. Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization;Chintakindi S;J Manuf Process,2022