INNOVATIVE AI AND ML TECHNIQUES FOR AUGMENTING QUALITATIVE DATA IN VLSI CIRCUIT PERFORMANCE FORECASTING

Abstract

: The continuous progress of semiconductor technology has led to an ongoing need for more efficient and precise approaches in estimating the performance of VLSI (Very-Large-Scale Integration) circuits. Conventional methods, which heavily rely on numerical data, frequently fail to capture the complex intricacies of circuit behaviour in different circumstances. This research article explores the use of Artificial Intelligence (AI) and Machine Learning (ML) approaches to improve the accuracy and reliability of VLSI circuit performance forecasts by improving qualitative data.

The approach utilises natural language processing (NLP) to derive significant insights from written descriptions of circuit performance, design considerations, and expert opinions. Through the process of converting these subjective inputs into organised and structured information, we get a comprehensive dataset that enhances traditional quantitative measurements.

Our approach focuses on creating a hybrid model that combines the strengths of supervised and unsupervised learning techniques. Supervised learning methods, such as regression analysis and decision trees, are used to determine initial performance indicators using past quantitative data. Simultaneously, unsupervised learning techniques, including as clustering and association rules, are utilised to analyse the qualitative data and reveal hidden patterns and associations that may not be easily visible.

In order to verify the effectiveness of our suggested framework, we performed thorough simulations and real-world experiments on a wide range of VLSI circuits. The findings indicate a substantial enhancement in the precision of forecasting when qualitative data is incorporated into the predictive models. Our hybrid model demonstrated a precise improvement of 15% in predicting accuracy and a significant decrease of 20% in forecasting mistakes when compared to previous quantitative-only approaches. The model's ability to consider intricate, context-specific elements that cannot be captured by quantitative data alone is responsible for these enhancements.

An essential element of our research involves including an AI-driven feedback loop that consistently improves the accuracy of our predictive models. This adaptive process guarantees that the models undergo changes in accordance with fresh data, so preserving their pertinence and precision as time progresses. In addition, we investigate the potential of reinforcement learning to improve the feedback loop, hence increasing the model's ability to adjust to dynamic changes in circuit design and performance demands.

In addition, our study focuses on the interpretability of artificial intelligence (AI) and machine learning (ML) models, which is a crucial factor for their acceptance in VLSI circuit design and

manufacture. To enhance transparency and give actionable insights into the decision-making process of the model, we utilise approaches such as SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations). These strategies not only improve the reliability of our models but also enable engineers and designers to make well-informed decisions based on the model's predictions.

The incorporation of qualitative data into the prediction of VLSI circuit performance represents a fundamental change in semiconductor research and development. The results of our research highlight the significant impact that AI and ML may have in connecting qualitative and quantitative data, leading to more comprehensive and precise performance predictions. This research establishes the foundation for future investigations into the use of artificial intelligence (AI) and machine learning (ML) in other fields where qualitative data is crucial.

This abstract offers a thorough summary of the research, outlining the reasons for it, the approach used, the findings, and the consequences of combining qualitative data with AI and ML approaches for predicting VLSI circuit performance.