  The gap between abstraction levels in analog design is a major obstacle for
advancing analog and mixed-signal (AMS) design automation and computer-aided
design (CAD). Intelligent models for low-level analog building blocks are
needed to bridge the accuracy gap between behavioral and transistor-level
simulations. The models should be able to accurately estimate the
characteristics of the analog block over a large design space. Machine learning
(ML) models based on actual silicon have the capabilities of capturing detailed
characteristics of complex designs. In this paper, a ML model called Artificial
Neural Network Metamodels (ANNM) have been explored to capture the highly
nonlinear nature of analog blocks. The application of these intelligent models
to multi-objective AMS block optimization is demonstrated. Parameterized
behavioral models in Verilog-AMS based on the ANN metamodels are constructed
for efficient AMS design exploration. To the best of the authors' knowledge
this is the first paper to integrate ANN models in Verilog-AMS, which is called
iVAMS 2.0. To demonstrate the application of iVAMS 2.0, this paper presents two
case studies: an operational amplifier (OP-AMP) and a phase-locked loop (PLL).
A biologically-inspired "firefly optimization algorithm" is applied to an
OP-AMP design in the iVAMS 2.0 framework. The optimization process is sped up
by 5580X due to the use of iVAMS with negligible loss in accuracy. Similarly,
for a PLL design, the physical design aware ANNs are trained and used as
metamodels to predict its frequency, locking time, and power. Thorough
experimental results demonstrate that only 100 sample points are sufficient for
ANNs to predict the output of circuits with 21 design parameters within 3%
accuracy. A proposed artificial bee colony (ABC) based algorithm performs
optimization over the ANN metamodels of the PLL.
