Compact models of transistors are essential for simulating and optimizing circuits with the use of SPICE simulation tool. Parameter extraction, which is calibrating these models, is essential to ensure their alignment with measured or simulated data. However, conventional parameter extraction methods are generally iterative and experience-dependent, requiring significant time and effort from modeling engineers. Moreover, as semiconductor devices and compact models become increasingly advanced, the need for a tailored extraction process for each model has become increasingly inefficient. To address the above challenges, this work proposes an agile transistor compact modeling framework, ATMAD. The proposed framework takes a compact model file and a set of electrical characteristic data as inputs, producing a calibrated model with minimal human intervention. ATMAD automatically retrieves the equations in the compact model and converts them into computational flow graphs, thus supporting different compact models with a generalized process. A graph unlooping technique is proposed to support automatic differentiation for compact models with implicit functions (e.g., series resistance and surface potential solving). Based on the computational flow graph, ATMAD adopts automatic differentiation technique to achieve automatic and parallel optimization of model parameters. The proposed ATMAD framework is validated on commonly-used compact models in academia and industry, showing its effectiveness for compact modeling for both I-V and C-V characteristics.

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.