在后穆尔时代设计半导体材料和设备,通过使用数据驱动的策略来应对计算挑战
Jiahao Xie1, Yansong Zhou2, Muhammad Faizan1
1State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
Nature computational science
|May 23, 2024
概括
数据驱动的计算方法加速了超越传统方法的半导体发现和设备优化. 这些策略探索材料,预测性能,并为未来的电子产品改进制造.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 电子工程 电子工程
背景情况:
- 电子行业面临着后摩尔定律时代的挑战,需要新的半导体材料和制造技术.
- 传统的试错方法用于材料发现和设备优化是耗时和低效的.
- 通过数据驱动策略增强的计算方法提供了一个强大的替代方案.
研究的目的:
- 突出数据驱动的计算框架在半导体发现和设备开发中的进步.
- 详细阐述这些框架在探索材料设计空间中的应用.
- 讨论使用这些方法预测半导体特性和优化设备制造.
主要方法:
- 审查和综合当前数据驱动的计算框架.
- 分析材料设计中的应用空间探索.
- 对半导体特性进行预测模型的检查.
- 对设备制造的优化技术的评估.
主要成果:
- 数据驱动的计算框架显著增强了对新型半导体材料的探索.
- 这些方法提高了预测半导体关键性质的准确性和速度.
- 通过计算建模加速了设备制造过程的优化.
- 成功的例子证明了加速发现和开发的潜力.
结论:
- 数据驱动的计算框架对于在后摩尔定律时代推进半导体研究和开发至关重要.
- 在数据集成,模型解释性和实验验证方面仍然存在挑战.
- 未来的机遇在于开发更复杂,集成和可解释的计算工具.
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