材料科學專題：半導體材料的微觀物理性質分析與納米制備技術研究及其在人工智能與大數(shù)據(jù)中的應用【大學組】

專業(yè)：工程

項目類型：國外小組科研

開始時間：2025年02月15日

是否可加論文：是

項目周期：7周在線小組科研學習+5周不限時論文指導學習

語言：英文

有無剩余名額：名額充足

建議學生年級：大學生

是否必需面試：否

適合專業(yè)：電子與計算機科學電子工程電子與通信工程信息工程電子電氣工程材料科學納米材料

地點：無

建議選修：分子工程學：大學化學及其應用

建議具備的基礎：電子工程、芯片設計、半導體材料、材料物理、物理電子學、微電子與固體電子學、光電子與光子學技術等專業(yè)或者希望修讀相關專業(yè)的學生； 學生需要具備基礎物理、電磁學、電路設計基礎

產(chǎn)出：7周在線小組科研學習+5周不限時論文指導學習 共125課時 項目報告 優(yōu)秀學員獲主導師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導（可用于申請） 結業(yè)證書 成績單

項目背景：人工智能芯片也被稱為AI加速器或計算卡，即專門用于處理人工智能應用中的大量計算任務的模塊。人工智能芯片可通過模仿人腦神經(jīng)網(wǎng)絡結構，用一條指令即可完成一組神經(jīng)元的處理。這一計算模式在做識別圖像等智能處理時，效率比傳統(tǒng)芯片高幾百倍。目前人工智能芯片已經(jīng)廣泛應用于圖像識別、語音識別、智能安防、智能駕駛、消費類電子等領域。Artificial intelligence chips are also called AI accelerators or computing cards, which are modules dedicated to processing a large number of computing tasks in artificial intelligence applications. Artificial intelligence chips can complete the processing of a group of neurons with one instruction by imitating the neural network structure of the human brain. This computing mode is hundreds of times more efficient than traditional chips when doing intelligent processing such as image recognition. At present, artificial intelligence chips have been widely used in image recognition, speech recognition, intelligent security, intelligent driving, consumer electronics and other fields.

項目介紹：本項目將從半導體中的固體物理基礎開始，主要包括半導體的電子帶結構和光相互作用/光學性質的原理，并特別關注低維半導體，如碳納米管、III-V量子阱、2D半導體、石墨烯以及量子點。隨后課程將介紹納米級器件，即p-n結，場效應晶體管以及傳感器，這部分課程的重點將是理解納米尺度的靜電學以及材料和器件中的傳輸理論，涵蓋納米級晶體管和量子受限材料中的彈道傳輸理論。還將討論存儲設備的基本概念，如果時間允許，也會討論光的物理學和運動傳感器。在介紹設備之后，課程將進一步介紹納米制造技術，包括光刻技術和半導體制造的進展，有助于制造用于現(xiàn)代計算機和服務器的最新高性能芯片。在納米制造和制造之后，導師將更多地介紹納米電子硬件的當前趨勢，用于人工智能和機器學習應用程序的大數(shù)據(jù)處理，包括低功耗/資源的邊緣計算。將詳細討論存儲設備、低功耗邏輯設備以及它們如何在模式識別等機器學習應用中協(xié)同工作。項目旨在于目為學生提供與計算過程和制造相關的基本物理框架，以及高性能節(jié)能大數(shù)據(jù)計算的硬件需求。討論的具體器件包括晶體管、存儲器件和傳感器(包括光電探測器和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.

項目大綱：納米級半導體及其特性 Nanoscale semiconductors and their properties 納米級電子器件和傳輸、光與物質相互作用 Nanoscale electronics devices and transport + Light-Matter interactions pn結型二極管p-n與光電器件 junction diode, Optoelectronic devices 晶體管、存儲設備與傳感器Transistors, memory devices and sensors 納米電子硬件的當前趨勢以及在人工智能和大數(shù)據(jù)中的應用 Current trends in nanoelectronics hardware and application to AI and big-data applications 學術研討1：教授與各組學生探討并評估個性化研究課題可行性，幫助學生明晰后續(xù)科研思路，內容詳見大綱 Research Workshop I 學術研討2：學生將在本周課前完成初步文獻回顧，教授將根據(jù)各組進度進行個性化指導，確保學生優(yōu)質的終期課題產(chǎn)出，內容詳見大綱 Research Workshop II 項目成果展示 Final Presentation 論文指導 Project Deliverables Tutoring