G. Kiran Kumar, Nandimandalam Sivarama Raju, Mohammad Nawaz, Nayudu Pradeep 03 04 In advanced IC manufacturing yield losses contribute to nearly 2030 of total fabrication cost with variations in die size and transistor scaling responsible for more than 60 of yield degradation while modern fabs generate terabytes of process data per production cycle making efficient prediction essential Existing manual and traditional yield prediction approaches rely on lowlevel statistical analysis and expertdriven rule formulation resulting in high time consumption poor scalability limited ability to model nonlinear interactions and reduced reliability at advanced technology nodes To address these limitations this work proposes a Quantum Neural Networkbased yield prediction framework for accurate estimation of die size and transistor scaling where the Integrated Chip IC manufacturing dataset is first subjected to comprehensive data preprocessing including normalization noise reduction and missingvalue handling followed by Exploratory Data Analysis EDA to identify key statistical trends and process correlations For performance comparison existing models such as the Restricted Boltzmann Machine RBM Regressor Gradient Boosting Regressor GBR and Extreme Gradient Boosting XGB Regressor are implemented to evaluate classical learning capabilities The proposed system integrates a Variational Quantum Neural Network VQNN for advanced quantum feature extraction leveraging parameterized quantum circuits to capture highdimensional nonlinear and entanglementbased relationships among manufacturing parameters which are difficult to model using classical methods These quantumenhanced features are then processed using an Ensemble Oblique Trees EOT regressor to achieve robust and highly accurate yield prediction significantly improving prediction accuracy adaptability and computational efficiency thereby enabling earlystage yield optimization and cost reduction in nextgeneration IC manufacturing Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026