Range-Aware Deep Learning Framework for Multi-Parameter Electrical Test Prediction in Semiconductor Manufacturing

Abstract

In semiconductor manufacturing, accurate prediction of electrical test (ET) parameters is essential for optimizing wafer quality and production efficiency. Traditional machine learning approaches rely on hard-to-obtain metrology data or handcrafted features, limiting scalability and practical applicability. In this work, we propose a deep learning framework that predicts multiple ET parameters using only readily available fabrication (FAB) process data. Our approach addresses two fundamental challenges: the categorical and sequential nature of FAB process data and the inherent range imbalance across ET parameters. We propose a feature extraction architecture that combines an input projection layer for handling categorical data with a one-dimensional convolutional neural network-based feature extractor designed to capture sequential patterns in FAB processes. To ensure balanced optimization across ET parameters with varying ranges, we introduce a range-aware loss function that assigns parameter-specific weights based on their value range. Experimental results on real-world semiconductor manufacturing data demonstrate that our proposed method achieves superior prediction accuracy compared to conventional methods, validating our framework's effectiveness in practical semiconductor manufacturing environments.