光电晶体管增益的正向与逆向虚拟计量：面向小生产数据集的分层、不确定性感知方法
Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical, Uncertainty-Aware Approach for Small Production Datasets
- Micro Nano Facility (MNF)（微纳米设施）
- Center for Sensors and Devices（传感器与器件中心）
- Fondazione Bruno Kessler (FBK)（布鲁诺·凯塞勒基金会）
- Department of Electronic Engineering, University of Rome Tor Vergata（罗马第二大学电子工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对小样本分层制造场景，提出分层不确定性感知的正向与逆向虚拟计量方法，实现光电晶体管增益的预测与目标配方搜索，发布数据集与代码支持可复现。
AI中文摘要：
硅双极型光电晶体管工艺流程的定制、优化与稳定化，需要耗费数月的洁净室时间才能完成器件测量，因此在生产批次前就能通过工艺参数预测器件增益的模型，其价值远超其精度本身。我们针对真实制造历史（单一器件的13至14次工艺批次）研究该问题，这是一个小样本、分层结构的场景，与传统虚拟计量的大语料库场景不同。通过分解器件增益的方差，我们发现约一半的方差存在于工艺批次之间而非批次内部，因此仅基于配方的预测存在固有边界。基于这些发现，我们提供了带有相对不确定性感知信号的正向增益预测器、返回目标增益配方的逆向搜索，以及作为所有方法基础的、针对制造嵌套物理实体（批次、晶圆、管芯）的多级数据质量评估，其中包含显式跨层级关联得分。标准化数据集和分析代码已发布，以支持完全可复现性。
英文摘要：
The customization, optimization and stabilization of the process flow of a silicon bipolar phototransistor commits months of cleanroom time before a finished device can be measured, so a model that predicts device gain from process parameters before a run has value out of proportion to its accuracy. We study this problem on a real fabrication history, thirteen to fourteen process runs of a single device: a small-sample, hierarchically structured setting unlike the large-corpus regime of conventional virtual metrology. Decomposing the variance of device gain, we find that roughly half of it lies between process runs rather than within them, so recipe-only prediction is bounded by construction. Building on these findings we provide a forward gain predictor with a relative, uncertainty-aware signal, an inverse search that returns recipes for a target gain, and, as the foundation for all of it, a multi-level data-quality assessment tailored to the nested physical entities of fabrication (batch, wafer, die) with an explicit cross-level linkage score. The normalized dataset and analysis code are released for full reproducibility.
