| 章礼华,张珺瑚,刘建,詹文法.抗过拟合的改进GBDT模型预测 IC熔丝修调码[J].电子测量与仪器学报,2026,40(4):262-269 |
| 抗过拟合的改进GBDT模型预测 IC熔丝修调码 |
| Over fitting-resistant improved GBDT model forpredicting IC fuse trimming codes |
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| DOI： |
| 中文关键词: 熔丝修调 修调码 预测 抗过拟合 模糊区域 |
| 英文关键词:fuse trimming trimming code prediction overfitting resistance fuzzy region |
| 基金项目:国家自然科学基金重大研究计划（92573109）、国家自然科学基金（62474002）、安徽省高校科研资助项目（2023AH050481）、安徽省高等学校质量工程项目(2023zygzts036,2024zybj022)资助 |
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| Author | Institution |
| Zhang Lihua | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, China |
| Zhang Junhu | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, China |
| Liu Jian | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, China |
| Zhan Wenfa | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, China |
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| 中文摘要: |
| 针对芯片制造过程中熔丝修调依赖逐次搜索索引修调码、循环读写频繁、测试时间长以及硬件资源消耗大的问题，在梯度提升多分类框架基础上，设计了一种面向熔丝修调码的单次预测模型。模型从结构层面引入抗过拟合机制，在残差学习过程中通过随机丢弃部分残差样本，弱化模型对异常样本和噪声样本的过度记忆；同时在叶节点输出阶段引入高斯扰动，使模型对数据分布波动保持更好的鲁棒性与泛化能力，并结合模糊区域判别思想，构建修调码模糊区域的数学描述，对靠近相邻边界的样本进行再判别和校正，从而有效降低相邻类别间的误分类概率。实现对相邻修调码不确定样本的精细区分。实验结果表明，所提出模型在准确率和稳定性方面均具有显著优势，在熔丝修调索引任务中，对32 bit修调码的整体预测准确率超过97.8%，R2指标达到0.99以上，显著缩短了测试时间并减少测试系统读写次数与硬件资源开销。 |
| 英文摘要: |
| To address the problems that fuse trimming in chip manufacturing still relies on sequential search of trimming codes, leading to frequent read-write operations, long testing time, and high hardware resource consumption, this paper designs a single-shot trimming-code prediction model based on a GBDT multi-classification framework. Overfitting-resistant mechanisms are introduced at the structural level: during residual learning, part of the residual samples is randomly discarded to weaken excessive memorization of abnormal and noisy samples, while Gaussian perturbations are injected into the leaf-node outputs to enhance robustness and generalization under fluctuations in data distribution. In addition, a fuzzy-region discrimination strategy is incorporated to construct a mathematical description of trimming-code fuzzy regions, enabling secondary discrimination and correction of samples located near adjacent class boundaries, thereby effectively reducing misclassification between neighboring categories and achieving refined identification of uncertain trimming codes. Experimental results show that, in the fuse-trimming indexing task, the proposed model achieves an overall prediction accuracy above 97.8% and an R2 value greater than 0.99, while significantly shortening test time and reducing read-write operations and hardware resource overhead. |
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