Origin of the Asymmetry in the Magnitude of the Statistical Variability of n- and p-Channel Poly-Si Gate Bulk MOSFETs

Asenov, A.; Cathignol, A.; Cheng, Binjie; McKenna, Keith P.; Brown, Andrew R.; Shluger, Alexander L.; Chanemougame, D.; Rochereau, K.; Ghibaudo, G.

doi:10.1109/led.2008.2000843

Cited by 49 publications

(26 citation statements)

References 17 publications

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“…In order to save computing time we have simulated square 42×42nm devices, which have SiO 2 oxide thickness of 1.7nm. Our previous studies of statistical variability in fresh n-and p-channel 45nm LP transistors yielded excellent agreement with measurements [12,13]. The simulations were carried out with the Glasgow 3D 'atomistic' simulator, which deploys fine grain discretisation and density gradient quantum corrections accurately resolving the contribution of individual discrete dopants and trapped charges [5].…”

Section: Simulation Methodologymentioning

confidence: 75%

“…Details about the device structure and doping profiles are published elsewhere [12,13]. We simulate NBTI variability associated with fixed/trapped random positive charges in concert with all other variability sources relevant for this technology.…”

Section: Simulation Methodologymentioning

confidence: 99%

“…The simulations were carried out with the Glasgow 3D 'atomistic' simulator, which deploys fine grain discretisation and density gradient quantum corrections accurately resolving the contribution of individual discrete dopants and trapped charges [5]. Random discrete dopants and line edge roughness were included in the simulations as the dominant sources of statistical variability in the fresh pMOSFETs, however This work was supported by the European FP7 Projects "Reliable and Variability tolerant System-on-a-chip Design in More-Moore Technologies (REALITY)" and "Terascale reliable adaptive memory systems" (TRAMS)" 978-3-9810801-7-9/DATE11/©2011 EDAA potential pinning at polysilicon grain boundaries is additionally included for the nMOSFETs [13].…”

Section: Simulation Methodologymentioning

confidence: 99%

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Statistical aspects of NBTI/PBTI and impact on SRAM yield

Asenov

Brown

Cheng

2011

2011 Design, Automation &Amp; Test in Europe

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Abstract-Quantitative simulations of the statistical impact of negative-bias-temperature-instability (NBTI) on pMOSFETs, and positive-bias-temperature-instability (PBTI) on nMOSFETs are carried out for a 45nm low power technology generation. Based on the statistical simulation results, we investigate the impact of NBTI and PBTI on the degradation of the static noise margin (SNM) of SRAM cells. The results indicate that SNM degradation due only to NBTI follows a different evolution pattern compared with the impact of simultaneous NBTI and PBTI degradation.

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Section: Simulation Methodologymentioning

confidence: 75%

Section: Simulation Methodologymentioning

confidence: 99%

Section: Simulation Methodologymentioning

confidence: 99%

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Statistical aspects of NBTI/PBTI and impact on SRAM yield

Asenov

Brown

Cheng

2011

2011 Design, Automation &Amp; Test in Europe

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“…However, in the pchannel MOSFET case the combined effect of just the RDD and LER is sufficient to reproduce accurately the experimental measurements. The reason for this is the presence of acceptor type interface states in the upper half of the bandgap which pin the Fermi level in the case of n-type poly-Si, and the absence of corresponding donor type interface states in the lower part of the bandgap which leaves the Fermi level unpinned in the case of p-type poly-Si [32].…”

Section: Validation Of the Drift-diffusion Simulation Methodologymentioning

confidence: 99%

Simulation of statistical variability in nano-CMOS transistors using drift-diffusion, Monte Carlo and non-equilibrium Green’s function techniques

et al. 2009

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In this paper, we present models and tools developed and used by the Device Modelling Group at the University of Glasgow to study statistical variability introduced by the discreteness of charge and matter in contemporary and future Nano-CMOS transistors. The models and tools, based on Drift-Diffusion (DD), Monte Carlo (MC) and NonEquilibrium Green's Function (NEGF) techniques, are encapsulated in the Glasgow 3D statistical 'atomistic' device simulator. The simulator can handle most of the known sources of statistical variability including Random Discrete Dopants (RDD), Line Edge Roughness (LER), Thickness Fluctuations in the Oxide (OTF) and Body (BTF), granularity of the Poly-Silicon (PSG), Metal Gate (MGG) and High-κ (HKG), and oxide trapped charges (OTC). The results of the statistical simulations are verified with respect to measurements carried out on fabricated devices. Predictions about the magnitude of the statistical variability in future generations of nano-CMOS devices are also presented.

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“…On the other hand, as the dimensions of MOSFETs are scaled into the nanometer regime, fluctuations in device characteristics due to variations in gate length, discrete dopant fluctuations, line-edge roughness, and so on have become serious problems [3][4][5]. Moreover, the presence of current noise in MOSFETs has recently become a problem even in digital circuits [6].…”

Section: Introductionmentioning

confidence: 99%

Capture/Emission Processes of Carriers in Heterointerface Traps Observed in the Transient Charge-Pumping Characteristics of SiGe/Si-Hetero-Channel pMOSFETs

Tsuchiya

Yoshida

Sakuraba

et al. 2011

KEM

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Abstract. Transient phenomena related to carrier capture/emission processes in interface traps were observed in the the charge pumping (CP) characteristics of SiGe/Si hetero-channel pMOSFETs, i.e., the CP characteristics were found to depend on the on/off time of the gate pulse. From these observations, time constants for the processes both in SiGe/Si heterointerface traps and in gate-oxide interface traps were derived. The time constant is considered to depend on the energy level of the interface traps that are present over a wide range within the energy gap. Therefore, these phenomena provide an interesting way of evaluating the discrete energy levels of interface traps in nanometer-scale devices containing only a few traps.

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Origin of the Asymmetry in the Magnitude of the Statistical Variability of n- and p-Channel Poly-Si Gate Bulk MOSFETs

Cited by 49 publications

References 17 publications

Statistical aspects of NBTI/PBTI and impact on SRAM yield

Statistical aspects of NBTI/PBTI and impact on SRAM yield

Simulation of statistical variability in nano-CMOS transistors using drift-diffusion, Monte Carlo and non-equilibrium Green’s function techniques

Capture/Emission Processes of Carriers in Heterointerface Traps Observed in the Transient Charge-Pumping Characteristics of SiGe/Si-Hetero-Channel pMOSFETs

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