HVL Semantic Binding for Early Functional Defect Detection

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Solution Overview

Problem

Detecting functional defects in software and hardware systems is challenging due to system complexity, elusive defect manifestation, limited resources, dynamic interactions, and interdisciplinary communication barriers, which complicates thorough defect detection and resolution.

Innovation Solution

A method using Hardware Verification Language (HVL) code to represent semantic behavior of design code, identify expressions and patterns associated with defects, and evaluate a bind of these expressions and patterns to detect functional defects, leveraging a Python framework for pattern identification and performance parameter evaluation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If comprehensive testing is performed to detect functional defects, then defect detection capability is improved, but resource consumption and time requirements increase

Engineering Contradiction:

Improvedefect detection capabilityVSAvoidtesting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by performing static analysis and generating test cases during the design code development phase, before actual execution. The system analyzes design code syntax and semantics upfront, identifies potential functional defects, and prepares verification strategies in advance, reducing the need for extensive later testing while improving defect detection capability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual mechanical testing processes with automated static analysis and AI-based verification systems. The system automatically parses design code, generates test cases, and evaluates functional defects without requiring extensive manual testing efforts, thereby improving detection capability while reducing time investment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive testing is performed to detect functional defects, then defect detection capability is improved, but resource consumption increases

Engineering Contradiction:

Improvedefect detection capabilityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent replaces resource-intensive manual testing and execution-based verification with automated static analysis tools and AI-based evaluation systems. The system consumes computational resources efficiently by analyzing code structure and semantics without requiring extensive hardware, human labor, or test execution infrastructure

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically analyzing design code, generating test cases, and identifying functional defects without requiring extensive external resources. The static analysis and AI verification processes are self-contained, reducing dependency on additional human resources, testing infrastructure, and operational overhead

Inventive Principle:

Principle #25Self-service

3Reliability

If static analysis and AI-based verification are used to detect defects early, then defect detection capability is improved, but system complexity increases

Engineering Contradiction:

Improvedefect detection capabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the defect detection system into distinct modular components: design code parser, static analysis engine, test case generator, AI-based verification module, and result evaluator. Each module performs a specific function independently, making the overall complex system manageable, maintainable, and scalable while improving defect detection capability

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250348644A1Methods and systems for detecting functional defects at designing stage of a design code

Publication Date: 2025.11.13 SAMSUNG ELECTRONICS CO LTD

AI summary

A method for detecting one or more functional defects in a design code includes generating, using processing circuitry, Hardware Verification Language (HVL) code representing semantic behaviour of design code, the generating including identifying one or more expressions associated with the one or more functional defects in the HVL code, identifying, using the processing circuitry, one or more patterns associated with the one or more functional defects in one or more second design code written in the first language, mapping, using the processing circuitry, the one or more expressions of the HVL code with the one or more identified patterns, forming, using the processing circuitry, a bind of the one or more expressions with the one or more patterns based on the mapping, and evaluating, using the processing circuitry, the bind to identify the one or more functional defects in the design code.