TDDB Kinetics and their Relationship with the E- and ¿E-models

Yiang, K. Y.; Yao, H. Walter; Marathe, A.

doi:10.1109/iitc.2008.4546957

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…The temperature dependence is modeled with the Arrhenius relationship in Eq. (1) [10,12], where k is the Boltzmann constant, q is the electronic charge, / B is the trap barrier height, e is the dielectric constant, p is a mathematical constant and the activation energy, E a / qðu B À ffiffiffiffiffiffiffiffiffiffiffiffiffi ffi qE=pe p Þ, is field dependent. Eq.…”

Section: Tddb Modelsmentioning

confidence: 99%

Backend dielectric reliability simulator for microprocessor system

Chen

Ahmed

Kim

et al. 2012

Microelectronics Reliability

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Section: Tddb Modelsmentioning

confidence: 99%

Backend dielectric reliability simulator for microprocessor system

Chen

Ahmed

Kim

et al. 2012

Microelectronics Reliability

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“…Fig. 2(b) shows that the normalized dynamic R dson initially increases gradually with increasing V drain under the conditions of 80% duty cycle and 100 kHz operating frequency (f s ), and then rises quickly when the V drain exceeds 200 V. This process is assumed to be thermally activated, with a rate constant that follows the Arrhenius' law [23]- [25]:…”

Section: Discussion On Activation Energy Of Device Degradationmentioning

confidence: 99%

Investigation on the Activation Energy of Device Degradation and Switching Time in AlGaN/GaN HEMTs for High-Frequency Application

Lei

Wang

Guo

et al. 2019

IEEE J. Electron Devices Soc.

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In this paper, the influence of traps on the dynamic on-resistance (R dson ) and switching time of AlGaN/GaN high-electron-mobility transistors is validated by means of a switching power converter with floating buck-boost topology. A new scheme based on the voltage-dependent dynamic R dson is proposed to extract the average activation energy of device degradation. The average activation energy obtained is 2.25 eV at 50-200 V and 2.60 eV at 200-600 V. In addition, the dynamic R dson is accurately extracted by a unique extraction circuit with high switching frequency up to 1 MHz and high off-state voltage up to 600 V. Meanwhile, the switching times at turn-on and turn-off transitions are captured in a floating buck-boost converter, showing that the transconductance decreases with increasing drain voltage. Furthermore, the effect of parasitic output capacitor on the dynamic R dson is investigated by an experimental method. Finally, a proper hard operating mode is proposed to alleviate the influence of the trapping effect on the performance of switching power converters.INDEX TERMS AlGaN/GaN HEMT, activation energy, dynamic on-resistance, switching time.RUI WANG received the B.S. degree from the School of Nanjing University of Posts and Telecommunications in 2017, and is currently pursuing the Ph.D. degree with the School of Electronic Science and Engineering, Nanjing University. Her research interests mainly focus on the relaibilty research of GaN-based power electronic devices, GaN-based power supply design, and so on.

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“…The electric field (E), temperature (T), and geometry (L i ) determine the characteristic lifetime, η. The temperature dependence is modeled with the Arrhenius relationship [13], where k is the Boltzmann constant. The experimental data used in our study comes from [10].…”

Section: Btddbmentioning

confidence: 99%

Comprehensive reliability and aging analysis on SRAMs within microprocessor systems

Liu

Chen

Kim

et al. 2015

Microelectronics Reliability

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a b s t r a c tA framework is proposed to analyze the impact of both Front End of the Line (FEOL) and Back End of the Line (BEOL) wearout mechanisms on memories embedded within state-of-art microprocessors. Our methodology finds the detailed electrical stress and temperature of each SRAM cell within a memory by running a variety of standard benchmarks. Combining the stress/thermal profiles and the wearout models, the performance degradation of SRAM cells for each wearout mechanism is studied. The lifetimes of the SRAM cells are then obtained when the performance metric degrades to a predefined threshold. The proposed work introduces a method to deal with the large volume of SRAM cells whose stress is non-uniform by partitioning the SRAM cells into different stress states, and generates the lifetime distribution of the memory system due to each wearout mechanism by combining the lifetimes of the cells, whose distributions vary with the stress received. Seven wearout mechanisms have been studied, namely, negative bias temperature instability (NBTI), positive bias temperature instability (PBTI), hot carrier injection (HCI), gate oxide breakdown (GOBD), backend dielectric breakdown (BTDDB), electromigration (EM), and stress-induced voiding (SIV).

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TDDB Kinetics and their Relationship with the E- and ¿E-models

Cited by 14 publications

References 6 publications

Backend dielectric reliability simulator for microprocessor system

Backend dielectric reliability simulator for microprocessor system

Investigation on the Activation Energy of Device Degradation and Switching Time in AlGaN/GaN HEMTs for High-Frequency Application

Comprehensive reliability and aging analysis on SRAMs within microprocessor systems

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