The role of the spacer oxide in determining worst-case hot-carrier stress conditions for NMOS LDD devices

2002 ◽  
Author(s):  
E.E. King ◽  
R.C. Lacoe ◽  
J. Wang-Ratkovic
Keyword(s):  
Stress Conditions ◽  
Worst Case ◽  
Hot Carrier ◽  
Hot Carrier Stress

scholarly journals Correlated Time-0 and Hot-Carrier Stress Induced FinFET Parameter Variabilities: Modeling Approach

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 657
Author(s):  
Alexander Makarov ◽  
Philippe Roussel ◽  
Erik Bury ◽  
Michiel Vandemaele ◽  
Alessio Spessot ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Simulation Model ◽  
Drain Current ◽  
Operating Regime ◽  
Statistical Simulation ◽  
Stress Conditions ◽  
Hot Carrier ◽  
Modeling Approach ◽  
Hot Carrier Stress ◽  
Random Traps

We identify correlation between the drain currents in pristine n-channel FinFET transistors and changes in time-0 currents induced by hot-carrier stress. To achieve this goal, we employ our statistical simulation model for hot-carrier degradation (HCD), which considers the effect of random dopants (RDs) on HCD. For this analysis we generate a set of 200 device instantiations where each of them has its own unique configuration of RDs. For all “samples” in this ensemble we calculate time-0 currents (i.e., currents in undamaged FinFETs) and then degradation characteristics such as changes in the linear drain current and device lifetimes. The robust correlation analysis allows us to identify correlation between transistor lifetimes and drain currents in unstressed devices, which implies that FinFETs with initially higher currents degrade faster, i.e., have more prominent linear drain current changes and shorter lifetimes. Another important result is that although at stress conditions the distribution of drain currents becomes wider with stress time, in the operating regime drain current variability diminishes. Finally, we show that if random traps are also taken into account, all the obtained trends remain the same.

Worst Case Stress Conditions for Hot Carrier Degradation with Technology Nodes from 0.35µm to 45nm

2019 ◽  
Vol 44 (1) ◽  
pp. 1151-1155 ◽  
Author(s):  
Zhaoxing Chen ◽  
Xiaoli Ji ◽  
Fen Yan ◽  
Yi Shi ◽  
Yongliang Song ◽  
...  
Keyword(s):  
Stress Conditions ◽  
Worst Case ◽  
Hot Carrier ◽  
Technology Nodes ◽  
Hot Carrier Degradation

Worst Case Stress Conditions for Hot Carrier Induced Degradation of p-Channel SOI MOSFETs

2006 ◽  
Author(s):  
D.P. Ioannou ◽  
R. Mishra ◽  
D.E. Ioannou ◽  
S.T. Liu ◽  
M. Flanery ◽  
...  
Keyword(s):  
Stress Conditions ◽  
Worst Case ◽  
Hot Carrier

Worst case stress conditions for hot carrier induced degradation of p-channel SOI MOSFETs

2006 ◽  
Vol 50 (6) ◽  
pp. 929-934 ◽  
Author(s):  
D.P. Ioannou ◽  
R. Mishra ◽  
D.E. Ioannou ◽  
S.T. Liu ◽  
H.L. Hughes
Keyword(s):  
Stress Conditions ◽  
Worst Case ◽  
Hot Carrier

HOT CARRIER STRESS INDUCED CHANGES IN MOST TRANSCONDUCTANCE STRUCTURE

1988 ◽  
Vol 49 (C4) ◽  
pp. C4-779-C4-782 ◽  
Author(s):  
C. BERGONZONI ◽  
R. BENECCHI ◽  
P. CAPRARA
Keyword(s):  
Hot Carrier ◽  
Hot Carrier Stress ◽  
Induced Changes

scholarly journals Robust Universal Inference

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 773
Author(s):  
Amichai Painsky ◽  
Meir Feder
Keyword(s):  
Robust Estimation ◽  
Minimax Estimator ◽  
Worst Case ◽  
Capacity Theory ◽  
Alternative Approach ◽  
True Model ◽  
Finite Set ◽  
Universal Prediction ◽  
Estimation Scheme

Learning and making inference from a finite set of samples are among the fundamental problems in science. In most popular applications, the paradigmatic approach is to seek a model that best explains the data. This approach has many desirable properties when the number of samples is large. However, in many practical setups, data acquisition is costly and only a limited number of samples is available. In this work, we study an alternative approach for this challenging setup. Our framework suggests that the role of the train-set is not to provide a single estimated model, which may be inaccurate due to the limited number of samples. Instead, we define a class of “reasonable” models. Then, the worst-case performance in the class is controlled by a minimax estimator with respect to it. Further, we introduce a robust estimation scheme that provides minimax guarantees, also for the case where the true model is not a member of the model class. Our results draw important connections to universal prediction, the redundancy-capacity theorem, and channel capacity theory. We demonstrate our suggested scheme in different setups, showing a significant improvement in worst-case performance over currently known alternatives.

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