MSc Compound Semiconductor Electronics (PhD Progression 1+3)

化合物半导体电子学理学硕士（博士升读 1 3）

• 批准号: 2882409
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882409
• 关键词: MSc; Compound; Semiconductor; Electronics; PhD

GaN is a high efficiency semiconductor with huge potential in upcoming technologies for 5G and 6G telecommunications networks. However, it is still hampered by material issues that result in trapping and self-heating and ultimately poor linearity. To resolve this requires a sound understanding at the device and circuit levels. The traditionally approach to designing a PA requires (i) an accurate large signal model of the transistor. Subsequently, (ii) a known topology of network with physical dimensions is tuned for realising matching impedances. Both steps, involve time-intensive iterative processes that rely on the experience of the designer.Deep convolutional neural network (CNN)-based EM emulators have transformed this design process in recent years, to allow rapid prototyping of the passive network [K. Sengupta 23, Beach 23], while relatively little is demonstrated by way of device modelling [ Guo 23]. In the latter work, the approach is based on a hybrid physical model that still relies on parameter extraction. Our goal is to explore new developments in AI such as reservoir computing for exploring large signal and matching networks. The model should be sophisticated to predict challenges of thermal and trapping constraints that currently affects GaN technology.

GaN 是一种高效半导体，在即将推出的 5G 和 6G 电信网络技术中具有巨大潜力。然而，它仍然受到材料问题的阻碍，导致陷阱和自热，并最终导致线性度不佳。要解决这个问题，需要对器件和电路级别有充分的了解。传统的 PA 设计方法需要 (i) 晶体管的精确大信号模型。随后，（ii）调整具有物理尺寸的已知网络拓扑以实现匹配阻抗。这两个步骤都涉及依赖于设计人员经验的时间密集型迭代过程。基于深度卷积神经网络 (CNN) 的 EM 模拟器近年来改变了这种设计过程，可以快速构建无源网络原型 [K. Sengupta 23，Beach 23]，而通过设备建模的方式展示的相对较少[Guo 23]。在后一项工作中，该方法基于仍然依赖于参数提取的混合物理模型。我们的目标是探索人工智能的新发展，例如用于探索大信号和匹配网络的水库计算。该模型应该很复杂，可以预测当前影响 GaN 技术的热和捕获约束的挑战。