Titlebook: NANO-CHIPS 2030; On-Chip AI for an Ef Boris Murmann,Bernd Hoefflinger Book 2020 Springer Nature Switzerland AG 2020 Nano-Electronics.Artifi

流動才波動

The New Era of Nano-chips: Green and Intelligent,ergy-efficient and intelligent nano-chip systems. It identifies five major thrust areas that are covered by 28 chapters written by world-leading experts: Robust and Efficient Silicon, Real-World Electronics, Neuromorphic Architectures, AI On-Chip and 3D Integration, and Man-Machine Cooperation.

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Real-World Electronics,r real-world-like functions. Relevant signals and information are perceived on logarithmic scales, where noticeable differences make up a constant percentage of the actual signal level. While this was realized in digital audio, it has been neglected in video because of linear charge-coupled-devices

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Nanolithography, reasons and for lithography limits, the roadmap for chip-product nodes has been re-written, so that node names have become quite different from minimum dimensions on-chip. 2018 became the introduction-year for Logic Industry Node “7?nm” with minimum features of 14?nm, and the IRDS Lithography roadm

對待

The Future of Ultra-Low Power SOTB CMOS Technology and Applications,n-thin-buried oxide (SOTB) technology is a CMOS device technology that uses fully depleted silicon-on-insulator (FDSOI) transistors with a thin buried oxide layer enabling enhanced back-bias controllability and that can be monolithically integrated with the conventional bulk CMOS circuits. It can si

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,Monolithic 3D Integration—An Update, covered the impact of the then recent breakthrough in bonding alignment on the ease of adapting monolithic 3D technology. Since then, the equipment vendors have made significant improvements in the precision of these bonders, making it a very attractive technology for Monolithic 3D integration. At

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Heterogeneous 3D Nano-systems: The N3XT Approach?,al languages, real-time sensor readings, contextual environments, or even brain signals. The computational demands of these abundant-data applications, such as deep learning-based AI, far exceed the capabilities of today’s computing systems and cannot be met by isolated improvements in transistor or

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High-Speed 3D Memories Enabling the AI Future,e technology of choice for memory in such AI systems. Yet, DRAM’s high energy?per bit and the diminishing scaling of DRAM represent a real challenge for the AI system’s future. This chapter covers solutions utilizing technologies similar to those currently used in 3D NAND for high speed memory.