Amorphous tris(8‐hydroxyquinoline)aluminum (AlQ3) nanoparticles can be grown directly into α‐phase crystalline nanowires in a one‐step heat treatment. At the most appropriate Ar pressure, heating time, and heating temperatures (between 150 and 190 °C), fine and long nanowires are obtained. The growth of the nanowires is dictated by the anisotropic bonding in α‐AlQ3 crystals. The growth mechanism is illustrated by the concept of nucleation and molecular migration. Two exotherms are revealed, from differential scanning calorimetry analyses, in the transformation process of AlQ3 amorphous nanoparticles to crystalline nanowires. The first exotherm is the transition from amorphous nanoparticles to the γ‐phase, and the second exotherm is the transition from the γ‐ to the α‐phase. By means of Kissinger plots, the activation energies for the crystallization of the γ‐phase and the transition from the γ‐ to the α‐phase are calculated, for the first time, to be 9.7 and 12.1 kJ mol–1, respectively. A blue‐shift and higher intensity of photoluminescence after heat treatment are also demonstrated.

To cope with last-minute design bugs and specification changes, engineering change order (ECO) is usually performed toward the end of the design process. This paper proposes an automatic ECO synthesis algorithm by interpolation. In particular, we tackle the problem by a series of partial rectifications. At each step, partial rectification can reduce the functional difference between an old implementation and a new specification. Our algorithm is especially effective for multiple error circuits. Experimental results show the proposed method is far superior to the most recent work and scales well on a set of large circuits.

We propose a robust circuit-based Boolean Satisfiability (SAT) solver, QuteSAT, that can be applied to complex circuit netlist structure. Several novel techniques are proposed in this paper, including: (1) a generic watching scheme on general gate types for efficient Boolean Constraint Propagation (BCP), (2) an implicit implication graph representation for efficient learning, and (3) careful engineering on the most advanced SAT algorithms for the circuit-based data structure. Our experimental results show that our baseline solver, without taking the advantage of the circuit information, can achieve the same performance as the fastest Conjunctive Normal Form (CNF)-based solvers. We also demonstrate that by applying a simple circuitoriented decision ordering technique (J-frontier), our solver can constantly outperform the CNF ones for more than 15+ times. With the great flexibility on the circuitbased data structure, our solver can serve as a solid foundation for the general SAT research in the future.

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