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Why Is Oxide-Trap Charge-Pumping Method Appropriate for Radiation-Induced Trap Depiction in MOSFET?

机译:为什么氧化物诱捕电荷泵方法适合MOSFET的辐射诱导陷阱描述?

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Radiation-induced traps, which are generally identified using specific extraction methods, play an important role in the reliability of MOS devices. In this paper, the oxide-trap-based-on-charge-pumping (OTCP) method is used to estimate radiation-induced oxide, interface, and border traps in complementary N- and P-MOS transistors. We emphasize on the critical comparison between the OTCP and classical methods like subthreshold slope (STS), midgap (MG), capacitance–voltage $(CV)$, dual-transistor CP (DTCP), and DT border trap (DTBT), giving a clear insight on the benefits and limitations of OTCP. According to experimental data, the OTCP method is often more accurate than the classical methods. On one side, OTCP offers more accurate densities of radiation-induced interface traps $(Delta N_{rm it})$ and border traps $(Delta N_{rm bt})$, while STS and MG overestimate $Delta N_{rm it}$ because both interface and border traps are sensed like interface traps. On the other side, OTCP estimates $Delta N_{rm it}$, $Delta N_{rm bt}$, and oxide trap $(Delta N_{rm ot})$ for N- and P-MOSFETs separately, while DTCP and DTBT give average densities for whole N- and P-MOS devices. Finally, $Delta N_{rm ot}$ obtained by OTCP is in excellent agreement with that given by $CV$. However, they show a slight discrepancy in the $D-n-nelta N_{rm it}$ extraction.
机译:通常使用特定的提取方法识别的辐射诱发陷阱在MOS器件的可靠性中起着重要作用。在本文中,使用基于电荷陷阱的氧化物陷阱(OTCP)方法来估计互补N-和P-MOS晶体管中的辐射诱导的氧化物,界面和边界陷阱。我们强调OTCP与经典方法之间的关键比较,例如亚阈值斜率(STS),中间间隙(MG),电容-电压$(CV)$,双晶体管CP(DTCP)和DT边界陷阱(DTBT),对OTCP的优点和局限性有清晰的了解。根据实验数据,OTCP方法通常比经典方法更准确。一方面,OTCP提供了更精确的辐射诱发的接口陷阱$(Delta N_ {rm it})$和边界陷阱$(Delta N_ {rm bt})$,而STS和MG高估了$ Delta N_ {rm } $,因为接口陷阱和边界陷阱都像接口陷阱一样被感知。另一方面,OTCP分别估算了N-和P-MOSFET的$ Delta N_ {rm it} $,$ Delta N_ {rm bt} $和氧化物陷阱$(Delta N_ {rm ot})$,而DTCP和DTBT给出了整个N-MOS和P-MOS器件的平均密度。最后,OTCP获得的$ Delta N_ {rm ot} $与$ CV $给出的非常一致。但是,它们在$ D-n-nelta N_ {rm it} $提取中显示出细微的差异。

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