In a previous study about a combinatorial optimization problem solver using neural networks, since the Hopfield method, convergence to the optimum solution sooner and with more certainty is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, dynamical systems have attracted attention recently. Therefore, we propose a "dynamical" combinatorial optimization problem solver using hysteresis neural networks. In this paper, the proposed system is evaluated by the N-Queen problem.

We have improved a compact model for the injection-enhancedinsulated-gate bipolar transistor for inverter circuit simulation. The holeaccumulation of floating-base region and potential change are modeled. It turned out that negative capacitance which occurs by floating-base region has the dependence of frequency. It is necessary to consider the frequency dependence of the total gate capacitance for transient simulation. We analyzed the relationship between negative gate capacitance and current rise rate at the switch turn-on timing and device structure. The development model simulation result is well reproduced $I_{ extrm{c}}$ and $V_{ extrm{ce}}$ of measurement data, and the switching loss calculation accuracy is improved.