  Logic obfuscation is introduced as a pivotal defense against multiple
hardware threats on Integrated Circuits (ICs), including reverse engineering
(RE) and intellectual property (IP) theft. The effectiveness of logic
obfuscation is challenged by the recently introduced Boolean satisfiability
(SAT) attack and its variants. A plethora of countermeasures has also been
proposed to thwart the SAT attack. Irrespective of the implemented defense
against SAT attacks, large power, performance, and area overheads are
indispensable. In contrast, we propose a cognitive solution: a neural
network-based unSAT clause translator, SATConda, that incurs a minimal area and
power overhead while preserving the original functionality with impenetrable
security. SATConda is incubated with an unSAT clause generator that translates
the existing conjunctive normal form (CNF) through minimal perturbations such
as the inclusion of pair of inverters or buffers or adding a new lightweight
unSAT block depending on the provided CNF. For efficient unSAT clause
generation, SATConda is equipped with a multi-layer neural network that first
learns the dependencies of features (literals and clauses), followed by a
long-short-term-memory (LSTM) network to validate and backpropagate the
SAT-hardness for better learning and translation. Our proposed SATConda is
evaluated on ISCAS85 and ISCAS89 benchmarks and is seen to defend against
multiple state-of-the-art successfully SAT attacks devised for hardware RE. In
addition, we also evaluate our proposed SATCondas empirical performance against
MiniSAT, Lingeling and Glucose SAT solvers that form the base for numerous
existing deobfuscation SAT attacks.
