国外小组科研—EE电子工程专题: 基于石墨烯、量子点等低维纳米半导体材料的AI芯片技术原理及其在人工智能与大数据中的应用【大学组】

开始日期：

2023年10月14日

专业方向：

理工

导师：

Deep（宾夕法尼亚大学 (UPenn) 教授）

课程周期：

7周在线小组科研学习+5周不限时论文指导学习

语言：

英文

建议学生年级：

大学生

项目产出：

7周在线小组科研学习+5周不限时论文指导学习 共125课时 项目报告 优秀学员获主导师Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表指导（可用于申请） 结业证书 成绩单

项目介绍：

本项目将从半导体中的固体物理基础开始，主要包括半导体的电子带结构和光相互作用/光学性质的原理，并特别关注低维半导体，如碳纳米管、III-V量子阱、2D半导体、石墨烯以及量子点。随后课程将介绍纳米级器件，即p-n结，场效应晶体管以及传感器，这部分课程的重点将是理解纳米尺度的静电学以及材料和器件中的传输理论，涵盖纳米级晶体管和量子受限材料中的弹道传输理论。还将讨论存储设备的基本概念，如果时间允许，也会讨论光的物理学和运动传感器。在介绍设备之后，课程将进一步介绍纳米制造技术，包括光刻技术和半导体制造的进展，有助于制造用于现代计算机和服务器的最新高性能芯片。在纳米制造和制造之后，导师将更多地介绍纳米电子硬件的当前趋势，用于人工智能和机器学习应用程序的大数据处理，包括低功耗/资源的边缘计算。将详细讨论存储设备、低功耗逻辑设备以及它们如何在模式识别等机器学习应用中协同工作。项目旨在于目为学生提供与计算过程和制造相关的基本物理框架，以及高性能节能大数据计算的硬件需求。讨论的具体器件包括晶体管、存储器件和传感器(包括光电探测器和MEMS)。 This is a online program starting with nanoelectronics devices and the role nanoscale electronics hardware plays in AI systems. After that the course will move into nanoscale devices namely p-n junctions, field-effect transistors as well as sensors. The focus in this part of the course will be to understand nanoscale electrostatics as well as transport theory in materials and devices. The theory of nanoscale transistor and ballistic transport in quantum confined materials will be covered. Basic concepts of memory devices will also be discussed. If time permits physics of light and motion sensors will also be discussed.After devices, the course will move into nanofabrication techniques including advances in lithography and semiconductor manufacturing that helps makes the latest high-performance chips used in modern computers and servers. After nanofabrication and manufacturing the course will more into and current trends in nanoelectronics hardware for handling big data for AI and machine learning applications including edge computing with low-power/resources. Detailed discussion on memory devices, low-power logic devices and how they work together in machine learning applications such as pattern recognition will be discussed. The aim of the course is to provide the student a fundamental physics framework pertaining to computing processes and fabrication as well as hardware needs for high performance energy efficient, big-data computation. Specific devices to be discussed include transistors, memory devices and sensors (including photodetectors and MEMS). The program aims to provide the students a high-level framework towards the understanding of nanoelectronics and optoelectronic devices. The course will help the students making informed decisions about their career choice and further having an upper hand when they take courses during graduate studies.