英特爾在半導體前沿技術儲備上

還有哪些制勝未來的殺手锏？

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英特爾技術與制造前沿研究項目

Intel’s Components Research teamfund sand works with universities and academic research consortia on forward-looking research topics such as Spintronics, and also on the understanding of fundamental science which is the basis of these forward-looking research.

英特爾前沿研究部門資助多家大學院校和學術機構，共同開展多種前沿研究課題，比如自旋電子以及這些前沿研究的基礎科學。

英特爾高級院士、技術與制造事業部制程架構與集成總監Mark T. Bohr

解讀英特爾前沿研究項目

Nanowire transistorsare being explored as a future option because the nanowire structure provides improves channel electrostatics that can enable further transistor gate length scaling.

納米線晶體管被認為是未來技術的一種選擇，因為納米線的結構可提供改進通道靜電，從而進一步實現晶體管柵極長度的微縮。

Silicon has always been the material used in MOSFET channels, butIII-V materialssuch as GaAs and InP have improved carrier mobility that can provide higher performance or the ability to operate transistors at lower voltage for lower active power.

硅是MOSFET通道中經常使用的材料，但是III-V 材料（如砷化鎵和磷化銦）改進了載流子遷移率，從而提供更高的性能或者能夠在更低的電壓和更低的有功功耗下運行晶體管。

3D stackingof silicon die can provide system integration opportunities to mix different technologies into a small form factor.

硅晶片的3D堆疊有機會實現系統集成，以便把不同的技術混裝到一個很小的地方。

A variety ofdense memoryoptions, including volatile and non-volatile memory, are being explored and developed.

多種不同的高密度內存選擇，其中包括易失性和非易失性存儲技術，正在探索和開發中。

Scaling interconnectsis just as important as scaling transistors on advanced process technologies. Novel materials and patterning techniques are being explored to enable dense interconnects.

對于精尖制程工藝來說，微縮互聯和微縮晶體管一樣重要。新的材料和圖案成形技術正在探索中，以支持高密度互聯。

Extreme Ultraviolet(EUV) lithography using a 13.5 nm wavelength is being developed to enable better scaling as today’s 193nm wavelength tools reach their scaling limit.

極紫外（EUV）光刻技術：采用13.5納米波長。由于當今的193納米波長工具已達到其微縮極限，該技術正在研發中以實現進一步的微縮。

Spintronicsis a beyond-CMOS technology that promises very dense and low power circuits that are an option when CMOS can no longer be scaled.

自旋電子是一種超越CMOS的技術，當CMOS無法再進行微縮的時候，這是一種選擇，可提供非常密集和低功耗的電路。

Neuromorphic computingis a different processor design and architecture approach for performing some computing functions with much better power efficiency than today’s computers.

神經元計算是一種不同的處理器設計和架構，能夠以比當前計算機高得多的能效執行某些計算功能。