基于神经进化的网络架构 半导体制造业的进化
Yen-Wei Feng1, Bing-Ru Jiang1, Albert Shihchun Lin1
1Institute of Electronics Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
ACS omega
|August 14, 2023
概括
神经进化 (NE) 自动优化模型架构用于智能制造,在半导体工艺建模中表现优于传统的多层感知器 (MLP) 模型. 这种方法有效地整合了物理约束,而不需要领域专业知识.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 开发精确的智能制造模型,特别是在半导体制造中,由于复杂的物理过程和需要广泛的试错,这是一项挑战.
- 传统方法往往难以将领域知识和物理约束有效地嵌入到模型架构中.
- 半导体制造涉及氧化,蚀刻,植入,化,扩散和化学机械抛光等复杂的步骤,每一步都需要精确的建模.
研究的目的:
- 自动搜索优化的模型架构,使用基于神经进化制造半导体的方法.
- 开发一个模型,可以快速构建合适的网络,同时整合实际的物理约束,而没有明确的领域知识提取.
- 将神经进化 (NE) 模型的性能与传统的多层感知子 (MLP) 模型进行比较,以预测 - (SiGe) 异质连接双极晶体管采集器电流.
主要方法:
- 利用神经进化 (NE) 方法自动发现最佳模型架构.
- 使用技术计算机辅助设计 (TCAD) 为 - (SiGe) 异质连接双极晶体管收集器电流生成数据作为目标数据集.
- 使用六个关键的过程参数 (氧化,干/湿蚀刻,植入,回火,扩散,化学-机械抛光) 作为模型输入.
主要成果:
- 与MLP模型相比,NE模型实现了明显较低的错误指标.
- NE模型的平均平方误差为3.285 × 10-7 (列车) 和1.661 × 10-7 (验证),而MLP则为1.317 × 10-6和7.215 × 10-7 .
- 测试组的平均绝对百分比误差为NE的0.097,大大优于MLP的0.216,表明更高的预测准确性和更好的物理洞察力提取.
结论:
- 基于神经进化的模型架构搜索为半导体制造中的智能制造应用提供了一个有希望和高效的方法.
- 与传统方法相比,NE方法成功地整合了物理约束,并实现了更快的交付时间.
- 该研究表明,与传统的MLP模型相比,NE模型在从数据中提取物理洞察力的能力更强.
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