One of the most notable features of nanometer scale CMOS technology is
the increasing magnitude of variability of the key device parameters
affecting performance of integrated circuits. The growth of variability
can be attributed to multiple factors, including the difficulty of
manufacturing control, the emergence of new systematic
variation-generating mechanisms, and most importantly, the increase in
atomic-scale randomness, where device operation must be described as a
stochastic process. In addition to wide-sense stationary stochastic
device variability and temperature variation, existence of
non-stationary stochastic electrical noise associated with fundamental
processes in integrated-circuit devices represents an elementary limit
on the performance of electronic circuits.
In an attempt to address these issues, Stochastic Process Variation in
Deep-Submicron CMOS: Circuits and Algorithms offers unique combination
of mathematical treatment of random process variation, electrical noise
and temperature and necessary circuit realizations for on-chip
monitoring and performance calibration. The associated problems are
addressed at various abstraction levels, i.e. circuit level,
architecture level and system level. It therefore provides a broad view
on the various solutions that have to be used and their possible
combination in very effective complementary techniques for both
analog/mixed-signal and digital circuits. The feasibility of the
described algorithms and built-in circuitry has been verified by
measurements from the silicon prototypes fabricated in standard 90 nm
and 65 nm CMOS technology.