263
Process Compact Modelling of Strain-Engineered MOSFETs
systems. (3) PCM is analogous to device compact models, which capture
electrical behaviour and can be derived from measurements or simulations.
SPICE process compact models (SPCMs) can be considered an extension
of PCMs applied to SPICE parameters. Using a global extraction strategy,
available from the Synopsys tool Paramos, pertinent compact SPICE model
parameters are simultaneously obtained as a polynomial function of process
parameter variations. The extraction procedure is performed using Paramos,
which will deliver an Extensive Markup Language (XML) file containing the
extracted SPICE model parameters. This methodology brings manufacturing to design, so that measurable process variations can be fed into design.
Additionally, design sensitivity to process can be fed back to manufacturing
so that product-dependent process controls can be performed. Here, the chosen SPICE model parameters (Y i ) are extracted as an explicit polynomial function of normalised process parameter variations (P j
), as shown in Equation
(9.1). Process parameter variations are normalised with respect to the corresponding standard deviation of the parameter, as shown in Equation (9.2).
Such a normalisation process enables the encryption of proprietary information like the absolute values of the process parameters.
y y
a p
i
i
ij
n
j
n
n
N
j
0
1
∑
∑
= +
=
(9.1)
where Y i is the nominal value of the ith model parameter, j is the jth process
parameter, N is the highest order of polynomial, a ij
n is the process coefficient
of the jth process parameter for the ith SPICE model parameter, and for order
n of the polynomial, p j
is the normalised process parameter, defined as
p
p p
j
j
j
j
0
=
−
σ
(9.2)
where p j is the jth value of the process parameter, p j
0 is the nominal value of
the jth process parameter, and σ j is the standard deviation of the jth process
parameter. In our study, we used BSIM4 SPICE model parameters as a quadratic function of process parameters. This model is easily scalable to higher
orders of polynomial (N) for higher accuracy of extraction [11]. The current
extraction strategy of the SPICE model parameters involves extraction of
nominal SPICE parameters y
i
0
( ) , followed by extraction of process coefficients a ij
n
( ) and reoptimised nominal values of SPICE parameters y
i
0
( ) . In the
following, we discuss the generation of process compact models (PCMs).
PCM Studio offers an accommodating front end to construct polynomial,
Hermite polynomial, and neural network PCMs, based on TCAD simulation data. Creating SPICE process compact models (SPCMs) is not directly
supported, though there is a plug-in for PCM Studio that simplifies the
Process Compact Modelling of Strain-Engineered MOSFETs
systems. (3) PCM is analogous to device compact models, which capture
electrical behaviour and can be derived from measurements or simulations.
SPICE process compact models (SPCMs) can be considered an extension
of PCMs applied to SPICE parameters. Using a global extraction strategy,
available from the Synopsys tool Paramos, pertinent compact SPICE model
parameters are simultaneously obtained as a polynomial function of process
parameter variations. The extraction procedure is performed using Paramos,
which will deliver an Extensive Markup Language (XML) file containing the
extracted SPICE model parameters. This methodology brings manufacturing to design, so that measurable process variations can be fed into design.
Additionally, design sensitivity to process can be fed back to manufacturing
so that product-dependent process controls can be performed. Here, the chosen SPICE model parameters (Y i ) are extracted as an explicit polynomial function of normalised process parameter variations (P j
), as shown in Equation
(9.1). Process parameter variations are normalised with respect to the corresponding standard deviation of the parameter, as shown in Equation (9.2).
Such a normalisation process enables the encryption of proprietary information like the absolute values of the process parameters.
y y
a p
i
i
ij
n
j
n
n
N
j
0
1
∑
∑
= +
=
(9.1)
where Y i is the nominal value of the ith model parameter, j is the jth process
parameter, N is the highest order of polynomial, a ij
n is the process coefficient
of the jth process parameter for the ith SPICE model parameter, and for order
n of the polynomial, p j
is the normalised process parameter, defined as
p
p p
j
j
j
j
0
=
−
σ
(9.2)
where p j is the jth value of the process parameter, p j
0 is the nominal value of
the jth process parameter, and σ j is the standard deviation of the jth process
parameter. In our study, we used BSIM4 SPICE model parameters as a quadratic function of process parameters. This model is easily scalable to higher
orders of polynomial (N) for higher accuracy of extraction [11]. The current
extraction strategy of the SPICE model parameters involves extraction of
nominal SPICE parameters y
i
0
( ) , followed by extraction of process coefficients a ij
n
( ) and reoptimised nominal values of SPICE parameters y
i
0
( ) . In the
following, we discuss the generation of process compact models (PCMs).
PCM Studio offers an accommodating front end to construct polynomial,
Hermite polynomial, and neural network PCMs, based on TCAD simulation data. Creating SPICE process compact models (SPCMs) is not directly
supported, though there is a plug-in for PCM Studio that simplifies the