700V smart trench IGBT with monolithic over-voltage and over-current protecting functions

An advanced 700V Smart Trench IGBT with monolithically integrated over-voltage and over-current protecting circuits is presented in this paper. The proposed Smart IGBT comprises a sense IGBT, a low voltage lateral n-channel MOSFET (M 1), an avalanche diode (D av), and poly-crystalline Zener diodes (ZD) and resistor (R poly). Mix-mode transient simulations with MEDICI have proven the functionalities of the protecting circuits when the device is operating under abnormal conditions, such as Unclamped Inductive Switching (UIS) and Short Circuit (SC) condition. A Trench IGBT process is used to fabricate this device with total 11 masks including one metal mask only. The characterizations of the fabricated device exhibit the clamping capability of the avalanche diode and voltage pull-down ability of the MOSFET. © 2012 IEEE.

Lyapunov functions for stable cascades and applications to global stabilization

This paper generalizes recent Lyapunov constructions for a cascade of two nonlinear systems, one of which is stable rather than asymptotically stable. A new cross-term construction in the Lyapunov function allows us to replace earlier growth conditions by a necessary boundedness condition. This method is instrumental in the global stabilization of feedforward systems, and new stabilization results are derived from the generalized construction.

Testing of the multi-objective alliance algorithm on benchmark functions

A new version of the Multi-objective Alliance Algorithm (MOAA) is described. The MOAA's performance is compared with that of NSGA-II using the epsilon and hypervolume indicators to evaluate the results. The benchmark functions chosen for the comparison are from the ZDT and DTLZ families and the main classical multi-objective (MO) problems. The results show that the new MOAA version is able to outperform NSGA-II on almost all the problems.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.