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使用混合密度网络对随机电子设备进行机器学习驱动的紧建模
Jack Hutchins1, Shamiul Alam1, Dana S Rampini2
1Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
Scientific reports
|March 17, 2024
概括
本研究引入了机器学习,特别是混合密度网络 (MDNs),以建模电子元件的可变行为. 这种方法准确地捕捉了诸如加热器冷子之类的设备中的随机动态,从而改善了电路设计和模拟.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 电子产品中的小型化和性能需求需要准确地建模设备变异性.
- 传统的确定性模型无法捕捉许多电子元件的随机性质.
- 电子元件的可变性对电路设计和模拟提出了重大挑战.
研究的目的:
- 开发一种创新的方法来建模电子设备的随机行为.
- 为了克服传统的决定性建模技术的局限性.
- 为了提高电子电路的紧型号的准确性和多功能性.
主要方法:
- 利用机器学习,特别是混合密度网络 (MDNs).
- 应用MDNs来表示和模拟电子设备的随机动态.
- 演示了加热器冷子的方法,以模拟它们的切换行为.
主要成果:
- 开发的模型成功地捕获了加热器冷子的随机切换动态.
- 实现了切换概率0.82%的平均绝对误差.
- 验证了MDNs在模拟设备可变性的有效性.
结论:
- 基于MDN的方法在建模随机电子设备行为方面取得了重大进展.
- 这种方法为改进电路模拟提供了准确和多功能紧型模型.
- 这些发现为在电子电路设计方面加强创新铺平了道路.
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