Sökning: "Nanoelektronik"
Visar resultat 1 - 5 av 10 avhandlingar innehållade ordet Nanoelektronik.
1. III-V Nanowire MOSFET High-Frequency Technology Platform
Sammanfattning : This thesis addresses the main challenges in using III-V nanowireMOSFETs for high-frequency applications by building a III-Vvertical nanowire MOSFET technology library. The initial devicelayout is designed, based on the assessment of the current III-V verticalnanowire MOSFET with state-of-the-art performance. LÄS MER
2. Vertical Heterostructure III-V MOSFETs for CMOS, RF and Memory Applications
Sammanfattning : This thesis focuses mainly on the co-integration of vertical nanowiren-type InAs and p-type GaSb MOSFETs on Si (Paper I & II), whereMOVPE grown vertical InAs-GaSb heterostructure nanowires areused for realizing monolithically integrated and co-processed all-III-V CMOS.Utilizing a bottom-up approach based on MOVPE grown nanowires enablesdesign flexibilities, such as in-situ doping and heterostructure formation,which serves to reduce the amount of mask steps during fabrication. LÄS MER
3. Vertical III-V Nanowire MOSFETs
Sammanfattning : Vertical III-V nanowire MOSFETs are interesting candidates for future digital and analog applications. High electron velocity III-V materials allow fabrication of low power and high frequency MOSFETs. Vertical vapor-liquid-solid growth enables fabrication of axial and radial heterostructure nanowires. LÄS MER
4. Vertical III-V Nanowire Transistors for Low-Power Electronics
Sammanfattning : Power dissipation has been the major challenge in the downscaling of transistor technology. Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) have struggled to keep a low power consumption while still maintaining a high performance due to the low carrier mobilities of Si but also due to their inherent minimum inverse subthreshold slope (S ≥ 60 mV/dec) which is limited by thermionic emission. LÄS MER
5. Vertical III-V Nanowires For In-Memory Computing
Sammanfattning : In recent times, deep neural networks (DNNs) have demonstrated great potential in various machine learning applications,such as image classification and object detection for autonomous driving. However, increasing the accuracy of DNNsrequires scaled, faster, and more energy-efficient hardware, which is limited by the von Neumann architecture whereseparate memory and computing units lead to a bottleneck in performance. LÄS MER