开发GUI驱动的AI深度学习平台,用于预测风扇外晶圆级包装的曲面行为
Ching-Feng Yu1, Jr-Wei Peng2, Chih-Cheng Hsiao2
1Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan.
Micromachines
|March 27, 2025
概括
这项研究引入了一个AI平台,用于预测风扇外晶圆级包装 (FOWLP) 中的曲面. 它的用户友好的界面使得工程师可以使用深度学习,提高电子包装设计效率.
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:
- 预测风扇外晶片级包装 (FOWLP) 中的曲面对于传统的电子工程师来说是一个挑战.
- 实施人工智能驱动的模型需要专门的编程和算法专业知识,限制了可访问性.
研究的目的:
- 开发一个可访问的AI预测平台,用于FOWLP中的曲面行为.
- 为非专家用户简化深度学习模型的设计,培训和操作.
主要方法:
- 开发基于深度学习的AI预测平台.
- 将图形用户界面 (GUI) 集成到抽象复杂的人工智能过程中.
- 针对大型数据集的自动功能提取,数据清理和模型训练.
主要成果:
- 该平台使用户能够在没有广泛的编码知识的情况下配置和运行AI预测.
- 对FOWLP架构的准确和可靠的曲面预测是通过案例研究实现的.
- 证明了设计效率的提高和减少电子包装中的错误.
结论:
- 人工智能平台提高了电子包装设计的深度学习的可访问性.
- 它为优化FOWLP设计和提高系统性能提供了宝贵的工具.
- 由GUI驱动的平台促进了电子包装技术的进一步进步.
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