It is always highly desired to have a well-defined relationship between the chemistry in semiconductor processing and the device characteristics. With the shrinkage of technology nodes in the semiconductors roadmap, it becomes more complicated to understand the relation between the device electrical characteristics and the process parameters such as oxidation and wafer cleaning procedures. In this work, we use a novel machine learning approach, i.e., physics-assisted multitask and transfer learning, to construct a relationship between the process conditions and the device capacitance voltage curves. While conventional semiconductor processes and device modeling are based on a physical model, recently, the machine learning-based approach or hybrid approaches have drawn significant attention. In general, a huge amount of data is required to train a machine learning model. Since producing data in the semiconductor industry is not an easy task, physics-assisted artificial intelligence has become an obvious choice to resolve these issues. The predicted C – V uses the hybridization of physics, and machine learning provides improvement while the coefficient of determination ( R 2 ) is 0.9442 for semisupervised multitask learning (SS-MTL) and 0.9253 for transfer learning (TL), referenced to 0.6108 in the pure machine learning model using multilayer perceptrons. The machine learning architecture used in this work is capable of handling data sparsity and promotes the usage of advanced algorithms to model the relationship between complex chemical reactions in semiconductor manufacturing and actual device characteristics. The code is available at .

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Machine learning (ML) compact device models (CM) have emerged as an alternative to physics-based CMs. ML CMs can find a mathematical model close to the device characteristics without much prior knowledge, which saves the time of model formation. Additionally, versatile capabilities such as process-awareness, model merging, and fitting new technologies, promote the usage of ML CMs. While ML CMs draw great attention in CAD, their convergence in SPICE has not been carefully studied. Here different activation functions are used to create ML CMs, and then the circuit convergence is tested. We found that inverse square root unit (ISRU) activation has the best convergence. Besides, gate-to-source and gate-to-drain capacitance is founded to benefit the convergence in transient analysis. The circuit convergence rate is 100% for ISRU, sigmoid, and tanh when the capacitor is present. On the other hand, ISRU significantly outperforms other activation functions in DC sweep, achieving 81% convergence. If quasi-static transient analysis is employed to replace DC sweep, 100% convergence is achieved by ISRU. Due to its superior convergence, ISRU is the most promising for future ML CMs in SPICE.

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