  We demonstrate that data-driven system identification techniques can provide
a basis for effective, non-intrusive model order reduction (MOR) for common
circuits that are key building blocks in microelectronics. Our approach is
motivated by the practical operation of these circuits and utilizes a canonical
Hammerstein architecture. To demonstrate the approach we develop a parsimonious
Hammerstein model for a non-linear CMOS differential amplifier. We train this
model on a combination of direct current (DC) and transient Spice (Xyce)
circuit simulation data using a novel sequential strategy to identify the
static nonlinear and linear dynamical parts of the model. Simulation results
show that the Hammerstein model is an effective surrogate for the differential
amplifier circuit that accurately and efficiently reproduces its behavior over
a wide range of operating points and input frequencies.
