T1.4 Simulation of the device time-dependent variability: from the nanoscale properties
to device electrical performance
Global objective 1; subobjective 1.3
Description
In this task, the experimental results and models proposed in tasks T1.1 to T1.3 will be the inputs to a device
simulator, which will provide a statistically representative data base of the aging of devices (not feasible from
experimental tests, because of the extremely high cost in terms of number of devices and required time). Simulations
will be carried out at two different levels, and mostly focused on planar devices:
(i) 3D atomistic simulations
The underlying physics of the TDV sources will be studied by means of 3D atomistic simulations. The behavior of
individual defects (responsible of BTI and CHC degradation) will be reproduced in planar MOSFETs. In this way,
from the physics of the device, the statistical distributions of the main parameters that characterize the defects
behavior (such as charge capture and emission times) will be determined, which will be the inputs to the device level
simulations in (ii). The atomistic simulations will be performed using the BITLLES simulation package (a free software
developed by the NANOCOMP group at the Electronic Engineering Department of the UAB).
(ii) Device level simulations
The time-dependent variability of devices will be simulated using a commercial device simulation tool (in particular
the ATLAS simulator). To do so, an external Montecarlo module will be developed, which will contain the aging
models developed for BTI and CHC degradation (T1.2) with model parameter values experimentally determined from
previous tasks (T1.3) and/or from 3D atomistic simulations (this task). This module will generate random distributions
of defects at the interface and bulk of the gate dielectric, which will represent the aging of the device. The resulting
defect distributions will be introduced into ATLAS, who will be the responsible of providing the statistically distributed
I-V characteristics of the devices under study. Moreover, the effects of SEU in newer technologies is relatively
unknown. Some predictions point out to more sensitivity to SEU and DSET, but these newer structures can react
against radiation in a different way. These new structures must be analyzed in order to determine its performance in
front of radiation effects, and evaluate the advantages and disadvantages.SEUs effects will be then also simulated.
From the ATLAS simulations, the following kind of information will be obtained:
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The current-voltage characteristics of the MOSFETs, for as-grown and aged devices.
The time-dependent variability of the I-V characteristics introduced by BTI, CHC and TDDB.
SEUs effects characterization in advanced CMOS new devices (FinFETs)
Schedule:
M1-M24
People involved: UAB: Porti, Rodriguez, Porti, Velayudhan, Moras.
UPC: Calomarde, Moll, Rubio
Inputs
T111 Statistical information on the electrical properties of as-grown bulk devices at the nanoscale. Input to T1.4.
(M6)
T112 Statistical information on the spatial distribution and dependence on operation conditions (V, T) of the BTI and
CHC aging and BD of bulk devices at the nanoscale. Input to T1.2 and T1.4. (M12).
T122 Physics-based model for CHC degradation in planar devices. Input to T1.3, T1.4 and T2.1 (M15)
T132 Low-percentile tails of the distributions in the physical models for BTI and CHC degradation and TDDB. Input to
T1.4 and T2.1 (M18)
T133 Reliability data base, for model parameter extraction. Input to T1.4 (M18).
Deliverables:
T141
T142
T143
Improved physical understanding of the TDV. (M24)
Statistical Data base of time-dependent electrical characteristics of planar devices. Input to T2.1 and T3.5.
(M.21)
Models for SEU effects in newer technologies. Input to T2.3 (M18)