  A thermal simulation methodology is developed for interconnects enabled by a
data-driven learning algorithm accounting for variations of material
properties, heat sources and boundary conditions (BCs). The methodology is
based on the concepts of model order reduction and domain decomposition to
construct a multi-block approach. A generic block model is built to represent a
group of interconnect blocks that are used to wire standard cells in the
integrated circuits (ICs). The blocks in this group possess identical geometry
with various metal/via routings. The data-driven model reduction method is thus
applied to learn material property variations induced by different metal/via
routings in the blocks, in addition to the variations of heat sources and BCs.
The approach is investigated in two very different settings. It is first
applied to thermal simulation of a single interconnect block with similar BCs
to those in the training of the generic block. It is then implemented in
multi-block thermal simulation of a FinFET IC, where the interconnect structure
is partitioned into several blocks each modeled by the generic block model.
Accuracy of the generic block model is examined in terms of the metal/via
routings, BCs and thermal discontinuities at the block interfaces.
