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Kiyotaka Imai

Researcher at NEC

Publications -  78
Citations -  1448

Kiyotaka Imai is an academic researcher from NEC. The author has contributed to research in topics: CMOS & Transistor. The author has an hindex of 18, co-authored 77 publications receiving 1418 citations.

Papers
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Proceedings ArticleDOI

The impact of bias temperature instability for direct-tunneling ultra-thin gate oxide on MOSFET scaling

TL;DR: In this article, the authors present a new reliability scaling scenario for CMOS devices with direct-tunneling ultra-thin gate oxide, and show that the threshold voltage change caused by bias-temperature instability (BTI) limits the device lifetime.
Proceedings ArticleDOI

NBTI enhancement by nitrogen incorporation into ultrathin gate oxide for 0.10-/spl mu/m gate CMOS generation

TL;DR: In this article, the degradation of device reliability due to Negative Bias Temperature Instability (NBTI) of PMOSFETs with ultrathin gate oxide was investigated and it was experimentally demonstrated that the chemical reactions at the gate oxide/substrate interface and/or diffusion of hydrogen related species are the major cause of the NBTI.
Proceedings ArticleDOI

New analysis methods for comprehensive understanding of Random Telegraph Noise

TL;DR: Bias dependence of statistically extracted average trap number is discussed, with emphasis on the importance of undetectable traps on product reliability.
Proceedings Article

Single-charge-based modeling of transistor characteristics fluctuations based on statistical measurement of RTN amplitude

TL;DR: In this article, a single charge-based random fluctuation model suited for analyzing both random telegraph noise (RTN) and intrinsic channel transistors (UTB-SOI, FinFET etc) is proposed.
Proceedings ArticleDOI

Direct observation of RTN-induced SRAM failure by accelerated testing and its application to product reliability assessment

TL;DR: In this article, a new accelerated testing scheme for detecting SRAM bit failure caused by random telegraph noise (RTN) is proposed, which can accurately predict product reliability against RTN.