Autonomous experimentation has emerged as an efficient approach to accelerate the pace of materials discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials and nanoparticles, examples of autonomous tools for physical vapour deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous instrument for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of emission lines monitored during the co-sputtering from elemental Zn and Ti targets in N$_2$ atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined composition, by minimizing the absolute error between desired and measured emission signals. We validated our approach by autonomously fabricating Zn$_x$Ti$_{1-x}$N$_y$ films with deviations from the targeted cation composition within relative 3.5 %, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity depends non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

翻译：自主实验已成为加速材料发现进程的高效途径。尽管自主合成仪器在分子与高分子科学、杂化材料及纳米颗粒溶液处理领域已普及，但在半导体工业中至关重要的物理气相沉积自主工具实例仍较为稀缺。本文报道了一种用于溅射沉积成分可控薄膜的自主仪器设计与实现，该仪器采用由Python定制控制的高度自动化溅射反应器，结合光学发射光谱（OES）与贝叶斯优化算法。我们将X射线荧光测量的薄膜成分建模为N₂气氛下从Zn和Ti元素靶材共溅射过程中监测的发射谱线的线性函数。基于OES信息的贝叶斯控制算法通过最小化目标发射信号与实测信号之间的绝对误差，在溅射功率空间中导航以制备用户指定成分的薄膜。通过自主制备ZnₓTi₁₋ₓNᵧ薄膜（即使对于15 nm超薄膜），其目标阳离子成分偏差控制在相对3.5%以内，验证了该方法可在最小化人工干预条件下可靠合成特定成分薄膜。此外，该方法可拓展至等离子体强度与压力呈非线性关系，或元素粘附系数强烈依赖衬底温度等更复杂的合成实验场景。