CSE245: Computer-Aided
Circuit Simulation and
Verification
Lecture Note 5
Numerical Integration
Prof. Chung-Kuan Cheng
1
Numerical Integration: Outline
• One-step Method for ODE (IVP)
– Forward Euler
– Backward Euler
– Trapezoidal Rule
– Equivalent Circuit Model
• Convergence Analysis
• Linear Multi-Step Method
• Time Step Control
2
Ordinary Difference Equations
Solve Initial Value Problem (IVP) :
 dx (t )
 f ( x, t )

 dt
 x (t0 )  x0
in an interval [t 0 ,T] given the initial condition x0 .
N equations, n x variables, n dx/dt.
Typically analytic solutions are not available
 solve it numerically
3
Numerical Integration
 dx (t )
 f ( x, t )

 dt
 x (t0 )  x0
Forward Euler
Backward Euler
Trapezoidal
4
Numerical Integration: State Equation
Forward Euler
Backward Euler
5
Numerical Integration: State Equation
Trapezoidal
6
Equivalent Circuit Model-BE
• Capacitor
v (t  t )  v (t ) 
t
C
i(t  t )  v (t  t )
i(t  t )
C
t
 v (t )
C
t
+
i(t   t )
+
+
i(t   t )
v (t  t )
C
G eq 
v (t  t )
-
C
t
I eq 
-
C
t
v (t )
-
7
Equivalent Circuit Model-BE
• Inductor
i(t  t )  i(t ) 
t
L
v (t  t )
v (t  t )  i(t  t )
L
t
 i (t )
L
t
+
+
i(t   t )
+
i(t   t )
L
V eq 
L
t
i(t )
v (t  t )
v (t  t )
R eq 
L
t
8
Equivalent Circuit Model-TR
• Capacitor
v (t  t )  v (t ) 
t
2C
i(t  t )  v (t  t )
( i ( t )  i ( t   t ))
2C
t
 v (t )
2C
t
 i(t )
+
i(t   t )
+
+
i(t   t )
v (t  t )
C
G eq 
v (t  t )
-
2C
t
I eq 
-
2C
t
v (t )  i(t )
-
9
Equivalent Circuit Model-TR
• Inductor
i(t  t )  i(t ) 
t
2L
( v ( t )  v ( t   t ))
v (t  t )  i(t  t )
2L
t
 i(t )
2L
t
 v (t )
+
+
i(t   t )
+
i(t   t )
L
V eq 
2L
t
i(t )  v (t )
v (t  t )
v (t  t )
R eq 
2L
t
10
Summary of Basic Concepts
Trap Rule, Forward-Euler, Backward-Euler
All are one-step methods
xk+1 is computed using only xk, not xk-1, xk-2, xk-3...
Forward-Euler is the simplest
No equation solution
explicit method.
Backward-Euler is more expensive
Equation solution each step
implicit method
most stable (FE/BE/TR)
Trapezoidal Rule might be more accurate
Equation solution each step
implicit method
More accurate but less stable, may cause oscillation
11
Stabilities
Froward Euler
 x k  1  x k  hx k

 xk   xk


x k 1  x k  h  x k
j
h
-1
u n stab le
0
stab le
x k 1  (1  h  ) x k  (1  h  )
k 1
x0
12
FE region of absolute stability
Forward Euler
z  1  h  
ODE stability
region
Im(z)
-1
Difference Eqn
Stability region
Im   
1
Re(z)
2
t
Region of
Absolute
Stability
Re   
13
Stabilities
Backward Euler
j
 x k  1  x k  hx k  1

 x k 1   x k 1

x k 1  x k  h  x k 1

1
x k 1 
1  h
xk  (
h
stab le
1 - h
0
1
u n stab le
1
1  h
)
k 1
x0
14
BE region of absolute stability
Backward Euler
z  1  h 
1
Im   
Im(z)
-1
Difference Eqn
Stability region
1
Re(z)
Region of
Absolute
Stability
15
Stabilities
Trapezoidal
j
sta b le
h

 x k 1  x k  2 ( x k  x k 1 )

 xk   xk
x
  x k 1
 k 1


x k 1  x k 
1

x k 1 
1
h
2
h
2
h
2
1 +  h /2
-1
1 - h /2
0
1
u n sta b le
 ( x k  x k 1 )

1
xk  (

h
1
h
2
h
2

)
k 1
x0

16
Convergence
• Consistency: A method of order p (p>1) is
consistent if
• Stability: A method is stable if:
• Convergence: A method is convergent if:
Consistency + Stability
Convergence
17
A-Stable
• Dahlquist Theorem:
– An A-Stable LMS (Linear Multi-Step) method
cannot exceed 2nd order accuracy
• The most accurate A-Stable method
(smallest truncation error) is trapezoidal
method.
18
Convergence Analysis: Truncation Error
• Local Truncation Error (LTE):
– At time point tk+1 assume xk is exact, the difference between
the approximated solution xk+1 and exact solution x*k+1 is
called local truncation error.
– Indicates consistency
– Used to estimate next time step size in SPICE
• Global Truncation Error (GTE):
– At time point tk+1, assume only the initial condition x0 at time t0
is correct, the difference between the approximated solution
xk+1 and the exact solution x*k+1 is called global truncation
error.
– Indicates stability
19
LTE Estimation: SPICE
• Taylor Expansion of xn+1 about the time point tn:
x(tn+1)=x(tn)+dx(tn)/dt∙h+d2x(tn)/dt2∙h2/2!+d3x(tn)/dt3∙h3/3!+…
• Taylor Expansion of xn about the time point tn+1:
x(tn)=x(tn+1)-dx(tn+1)/dt∙h+d2x(tn+1)/dt2∙h2/2!-d3x(tn+1)/dt3∙h3/3!+…
• Forward Euler
– Exercise x(tn+1)-xn+1=x(tn+1)-(xn+hdxn/dt)
• Backward Euler
– Exercise x(tn+1)-xn+1=x(tn+1)-(xn+hdxn+1/dt)
• Trapezoidal
– Exercise x(tn+1)-xn+1=x(tn+1)-[xn+(dxn/dt+dxn+1/dt)h/2]
LTE
20
Formula for pth order method
Formula E(x(t),h)=∑i=0,kaix(tn-i)+hbidx(tn-i)/dt
Let x(t)=[(tn-t)/h]p
E(x(t),h)=∑i=0,kai[(tn-tn-i)/h]p-pbi[(tn-tn-i)/h]p-1
=∑i=0,kaiip-pbiip-1
If the formula is a pth order method, we have
Case p=0: ∑i=0,kai=0
Case p=1: ∑i=0,kaii-bi=0
……
Case p: ∑i=0,k(aii-pbi)ip-1=0
LTE
21
Formula for pth order method: Example
Forward Euler: We have a0=1, a1=-1, b0=0, b1=-1
Case p=0: ∑i=0,kai=0
Case p=1: ∑i=0,kaii-bi=0
Case p=2: ∑i=0,k(aii-2bi)i=-1+2=1
Backward Euler: We have a0=1, a1=-1, b0=-1, b1=0
Case p=0: ∑i=0,kai=0
Case p=1: ∑i=0,kaii-bi=0
Case p=2: ∑i=0,k(aii-2bi)i=-1+2=1
Trapezoidal Rule: We have a0=1, a1=-1, b0=-1/2, b1=-1/2
Case p=0: ∑i=0,kai=0
Case p=1: ∑i=0,kaii-bi=0
Case p=2: ∑i=0,k(aii-2bi)i=0
Case p=3: ∑i=0,k(aii-3bi)i2=-1+3/2=1/2
LTE
22
Formula for pth order method:
Variables
There are 2(k+1)-1 unknowns (a0=1), and p+1 equations.
Thus, we need 2(k+1)-1 ≥ p+1
In other words, k ≥ p/2
LTE
23
Formula for pth order method: Local
Truncation Error
By Taylor’s expansion, we have
x(t)=x(tn)+x’(tn)(t-tn)+…+1/(p+1)! x(p+1)(tn)(t-tn)p+1+…
Thus, the error of pth order method is
∑i=0,k{ai[(tn-tn-i)/h]p+1-pbi[(tn-tn-i)/h]p}1/(p+1)! x(p+1)(tn)hp+1+O(hp+2)
Let us set Ep+1={∑i=0,kaiip+1-pbiip}/p!
Method
FE
BE
TR
a0
1
1
1
a1
-1
-1
-1
b0
0
-1
-1/2
b1
-1
0
-1/2
Ep+1
E2=1/2
E2=1/2
E3=1/12
LTE
24
Time Step Control: SPICE
• We have derived the local truncation error
the unit is charge for capacitor and flux for inductor
• Similarly, we can derive the local truncation error in
terms of
(1)
the unit is current for capacitor and voltage for inductor
• Suppose ED represents the absolute value of error that is
allowed per time point. That is
together with (1) we can calculate the time step as
25
Time Step Control: SPICE (cont’d)
• DD3(tn+1) is called 3rd divided difference, which is
given by the recursive formula
26
Multiple Step Integration: Stability
For a system x’=λx, let q= λh.
The integration formula is ∑i=0,kaixn-i+hbix’n-i=0.
We set (a0+qb0)zk+ (a1+qb1)zk-1 …+(ak+qbk)=0
There are k roots, ri, of the polynomial eq.
The generic solution is xn=c1r1n+c2r2n+…+ckrkn
but for multiplicity root, we have
xn=…+(ci0+ci1n+…+cimnm-1)rin+…
If |ri|< 1 for all i, the system is stable
Else for |ri|= 1 but not multiplicity root, the
27
system remains stable
Multiple Step Integration: Stability
For a system x’=λx, let q= λh.
The integration formula is ∑i=0,kaixn-i+hbix’n-i=0.
We set (a0+qb0)zk+ (a1+qb1)zk-1 …+(ak+qbk)=0
Examples using FE, BE, TZ methods
Method
a0
a1
b0
b1
root
FE
BE
TR
1
1
1
-1
-1
-1
0
-1
-1/2
-1
0
-1/2
1+q
1/(1-q)
(1+q/2)/(1-q/2)
28
Multiple Step Integration: Stability
For a system x’=λx, let q= λh.
The integration formula is ∑i=0,kaixn-i+hbixn-i=0.
A Stability: The system is stable for all
Real(q)≤ 0.
Dahlquist’s barrier:
– An A-Stable LMS (Linear Multi-Step) method
cannot exceed 2nd order accuracy
29