用机器人与多传感器系统实现大尺寸晶圆的高精度自动检测。
Enhancing Thin-Film Wafer Inspection With A Multi-Sensor Array And Robot Constraint Maintenance
- 通过学习约束流形控制机器人精准移动晶圆至校准位置。
- 静态测量误差小于2%,动态检测角度偏差超0.5°时仍能稳定监测。
- 适合R2R连续制造中位移变化的实时检测,可扩展性强。
在涂覆制造中,大面积基底上的薄膜检测对保证产品质量至关重要;然而,现有检测设备限制导致在大范围区域内精确检测面临重大挑战。自动化检测过程中还需对硅晶圆施加运动约束,以确保测量精度并防止损伤。此外,卷对卷(Roll-to-Roll, R2R)制造等新兴应用也带来了涂层厚度检测的新难题。本文提出一种自主检测系统,采用机器人操作臂结合新型学习约束流形,将晶圆精确定位至校准点,并配备可扩展的多传感器阵列。实验表明,该操作臂可在遵守运动约束条件下完成所需动作;传感器阵列在静态测量时相对于商用反射仪误差低于2%;利用操作臂动态检测时,能在1406个数据点上监测到超过0.5°的角度偏移,同时保持均方根误差(RMSE)和决定系数(R²)稳定。这些特性对R2R制造过程中的位移变化检测具有潜在价值。
原文摘要 · Abstract (English)
Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality; however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of $<2\%$ compared to a commercial reflectometer, and through the use of a manipulator can dynamically detect angle variations $>0.5^\circ$ from the calibration point whilst monitoring the RMSE and $R^2$ over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes.
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