AVS 72 Session AIML-ThP: AI/ML/Autonomous Experimentation for Thin Films Processing Poster Session

The semiconductor industry is currently undergoing rapid transformation driven by the expansion of artificial intelligence (AI) technologies. AI is not only increasing the demand for advanced semiconductor devices, but is also becoming a key enabler of innovation in semiconductor manufacturing processes. As semiconductor devices become more sophisticated with higher integration density and smaller process nodes, wafer fabrication costs continue to rise significantly. Consequently, unexpected process failures or abnormal equipment conditions can result in substantial financial losses and reduced production yield.

Despite the increasing complexity of semiconductor manufacturing, many plasma-based processes such as etching and ashing still lack direct in-situ methods to determine whether the process is proceeding normally in real time. In current manufacturing environments, abnormal process conditions are typically monitored through post-process sampling inspections and offline metrology techniques. However, these approaches have inherent limitations, including delayed feedback, increased inspection costs, and the risk of missing transient process anomalies.

To address these challenges, this study proposes an AI-based process monitoring framework for semiconductor ashing equipment. The proposed approach utilizes both equipment sensor signals and Optical Emission Spectroscopy (OES) measurement data collected during the ashing process to estimate the ashing rate and detect abnormal process conditions in real time. By integrating multiple process-related data sources, the model aims to improve process visibility and enable early detection of equipment or process deviations.

For the development of the predictive models, machine learning methodologies including XGBoost and Long Short-Term Memory (LSTM)-based neural network algorithms were investigated and compared. Experimental results demonstrated that the XGBoost-based models achieved the best overall performance among the evaluated approaches. The proposed model achieved an ashing rate estimation accuracy of 89% and a process anomaly detection rate of 95%, indicating strong potential for practical deployment in semiconductor manufacturing environments.

The results of this study suggest that AI-driven virtual metrology and anomaly detection technologies can significantly enhance process stability, reduce inspection dependency, and minimize yield loss in advanced semiconductor fabrication processes.

Targeted phase control in quasi-2D halide perovskites (HPs) is essential for tailoring their optoelectronic properties, yet remains challenging since phase formation is governed by the coupled effects of spacer chemistry, precursor assembly, and crystallization kinetics. Despite rapid advances in quasi-2D HPs, the identification of spacer design rules still relies largely on manual trial-and-error synthesis, limiting mechanistic understanding and reproducible phase selection. Here, we present a multimodal, closed-loop autonomous experimentation workflow for targeted phase selection in quasi-2D HPs using co-space engineering. We investigate a ternary 3D:2D compositional space composed of 3D FAPbI₃ and two Dion–Jacobson co-spacers, 1,4-butanediammonium (BDA) and 3-(aminomethyl)piperidinium (3AMP), to identify compositions that converge toward a stable 3D-like phase. In situ photoluminescence (PL) spectroscopy reveals that BDA promotes rapid crystallization and early formation of 3D-like emissive domains, whereas 3AMP slows crystallization and favors the persistence of lower-n quasi-2D phases. By systematically regulating the BDA:3AMP co-spacer ratio, the crystallization pathway can be tuned to balance fast framework formation with sufficient structural relaxation, enabling convergence toward the target phase. These kinetic insights are integrated with automated high-throughput synthesis, time-dependent PL characterization, X-ray diffraction similarity scoring, and Gaussian process–Bayesian optimization to navigate phase homogeneity and stability. This work establishes co-spacer engineering as a design strategy for targeted phase selection in kinetically governed quasi-2D HPs.