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This paper presents an advanced analytical neuro–space mapping (neuro‐SM) technique for accurate and efficient modeling of transistor devices. This is an improvement over the existing neuro‐SM, which aims to use neural networks to map a given approximate device model towards an accurate model. The proposed neuro‐SM retains the ability of the existing neuro‐SM in modifying the voltage relationship...
In this paper, an advanced Neuro-Space Mapping (SM) modeling technique for nonlinear device modeling is proposed. By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. The novelty of our work is to introduce a Neuro-SM model combining separate mappings for voltage and current...
This paper analyzes the pick-and-place process of the flip-chip technology, addressing two different control goals. Iterative learning control (ILC) is adopted to enhance the point-to-point positioning accuracy in the IC chip picking procedure. Furthermore, a trajectory modification is proposed for the traditional soft landing process, hence improving the efficiency of the IC chip placing procedure...
Traditionally, scientists preferred to design a neural network controller with sufficient neurons to satisfy realistic or simulational control requirements. Controllers derived from this methodology usually suffer tremendous training time and complicated neural network structure. Consequently, we decided to utilize ensemble theory which aims at replacing a complex object by effectively combining simpler...
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