Prof. D. Zhou
UT Dallas
Analog Circuits Design Automation
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Design Optimization
 Analog circuit design automation
 For a design
 Determine the specs
 Choose the intended manufacture process
 Choose the circuit topology
 Determine the variables and their “ranges”
 Transistor size, input and supply voltage, noise and etc.
 Choose the simulation tool
 SPICE, mixed signal and etc.
 Construct the objective functions and constraints
 Choose an efficient optimization method
Analog Circuits Design Automation
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Z.Yan, P.Mak, M.Law, R.P.Martins, "A 0.016-mm^2 144µW Three-Stage Amplifier
Capable of Driving 1-to-15 nF Capacitive Load With> 0.95-MHz GBW,"IEEE Journal
of Solid-State Circuits, vol.48, no.2, pp.527,540, Feb. 2013.
 Performance Concerned: minimize current consumption
 Parameter Space: device dimensions
 Constraints: design specifications
Manual Design
TT,27°C
FF,-40°C
SS,125°C
σ / Mean
GBW (MHz)
≥ 0.92
1.17
0.7
≤ 25.8%
PM (Degree)
≥ 52.5
51.8
55.5
≤ 3.7%
GM (dB)
≥ 19.5
21.2
18.5
≤ 6.95%
SR+(V/μs)
≥ 0.18
0.26
0.14
≤ 31.6%
SR- (V/μs)
≥ 0.20
0.26
0.11
≤ 39.7%
1% Ts+(μs)
≤ 5.17
4.07
6.78
≤ 25.5%
1% Ts- (μs)
≤ 5.71
3.80
9.02
≤ 42.7%
Min IQ (µA)
≤ 69.2
72.1
71.7
≤ 2.2%
 Two features make it outperform other methods
The probability for hitting a
• “Region hit” issue vs. “Point hit” issue
region is much larger than
• Guided search vs. random and independent search hitting a point!
Sample points
local optimum
global optimum
local optimum
Region of
attraction
global optimum
Start point
MC method used to find the global optimum
None of 200 Monte Carlo sample
points exactly hits the global
optimum.
MGO method used to find the global optimum
Once a start point hits the region containing
the global optimum, the global optimum
can be found easily by a local optimization
search.
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Eason’s function
Rastrigin’s function
Six-hump camel back’s function
Genetic, simulated annealing and particle swarm methods are using MATLAB build-in functions. The results are based on an average of 10 trials for each method.
*Data source: Marcin Molga and Czeslaw Smutnicki, “Test functions for optimization needs,” in 2005.
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