机器学习驱动的道厚度优化在双层氧化物薄膜晶体管中,以提高先进的电气性能
Jiho Lee1, Jae Hak Lee2,3, Chan Lee4
1Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Republic of Korea.
贝叶斯优化 (BO) 通过高效优化双层氧化物半导体薄膜晶体管 (OS TFTs) 加快电子设备设计. 这种机器学习方法可以显著减少实验试验,同时提高设备性能.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 机器学习 (ML) 为改进电子设备设计提供了自适应式学习.
- 优化复杂的多层半导体结构存在重大挑战.
- 传统的设计方法涉及广泛的试错,增加时间和成本.
研究的目的:
- 应用贝叶斯优化 (BO) 来实现双层氧化物半导体薄膜晶体管 (OS TFT) 的高效设计.
- 管理半导体层之间的相互依赖,以优化设备特性.
- 为了同时提高场效应移动性 (μ) 和值电压 (Vth),同时最大限度地减少实验力度.
主要方法:
- 利用贝叶斯优化 (BO) 来建模和优化双层OS TFT结构.
- 集成场效移动性 (μ) 和值电压 (Vth) 作为关键性能指标.
- 采用定制的BO算法,可根据特定的设计偏好调节重量因子.
主要成果:
- 实现了一种双层OS TFT,具有36.1cm2V-1s-1.1的增强场效移动性.
- 在偏差应力下表现出良好的运行稳定性,具有最小的门电压转移.
- 将实验试验减少到仅15个数据集,显示出显著的效率提升.
- 通过对μ和Vth进行重量因子调整,成功地定制了BO模型.
结论:
- 贝叶斯优化为设计先进的OS TFT提供了一种强大而高效的方法.
- 开发的BO模型有效地处理复杂的材料相互依赖性,从而带来卓越的设备性能.
- 这种方法大大减少了实验工作量,并加速了电子设备的开发周期.
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