Variational Autoencoder for IC Vulnerability Prediction

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Solution Overview

Problem

Integrated circuits are vulnerable to optical fault injection attacks due to unknown vulnerable locations, making it difficult to design effective countermeasures without high computational overhead.

Innovation Solution

A method using a neural network, specifically a variational autoencoder, to predict vulnerable locations in integrated circuits by correlating the structural architecture of known vulnerable regions with unknown regions, allowing for redesign to avoid these vulnerabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manufacturers implement comprehensive security countermeasures against optical fault injection attacks, then the security of the integrated circuit is improved, but the device complexity and computational overhead increase

Engineering Contradiction:

ImprovesecurityVSAvoiddevice complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by using machine learning models to predict vulnerable regions before the actual attack occurs. The system analyzes the structural architecture of the integrated circuit in advance to identify regions that could be exploited by optical fault injection attacks, allowing security countermeasures to be implemented proactively rather than reactively. This reduces the need for comprehensive but complex countermeasures by focusing only on predicted vulnerable areas.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies local quality by shifting from comprehensive global security countermeasures to targeted local protections. Instead of implementing security measures across the entire circuit, the system identifies specific vulnerable regions through machine learning analysis and applies countermeasures only to those localized areas. This approach maintains high security effectiveness while significantly reducing overall device complexity and computational overhead.

Inventive Principle:

Principle #3Local quality

2Reliability

If manufacturers conduct thorough analysis to identify all vulnerable locations in the integrated circuit, then the security is improved, but the time required for analysis and design iteration increases

Engineering Contradiction:

ImprovesecurityVSAvoidtime

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual, time-consuming analysis methods with automated machine learning systems. Instead of requiring extensive human analysis to identify vulnerable locations, the system uses trained neural networks that can rapidly analyze the structural architecture of the integrated circuit and predict vulnerable regions. This substitution dramatically reduces the time required for security analysis while maintaining or improving detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent uses machine learning models to create virtual copies or representations of the integrated circuit's structural architecture. These digital models allow the system to analyze and predict vulnerable regions without physically modifying or extensively examining the actual circuit. The machine learning model learns from the circuit's architecture and can quickly identify vulnerable patterns, significantly accelerating the security analysis process compared to traditional manual inspection methods.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If traditional design methods are used without machine learning, then the manufacturing process remains simple, but the ability to predict vulnerable locations and implement targeted mitigations is limited

Engineering Contradiction:

Improveability to predict vulnerabilitiesVSAvoidcomputational overhead

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies parameter changes by transforming the analysis approach from traditional design parameters to machine learning-based prediction parameters. The system uses machine learning models that can process and interpret the structural architecture of the integrated circuit to predict vulnerable regions. This change in parameters allows for more accurate and adaptable vulnerability prediction while keeping the implementation relatively simple by leveraging existing ML frameworks and techniques.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250199061A1Method of Identifying Vulnerable Regions in an Integrated Circuit

Publication Date: 2025.06.19 BATTELLE MEMORIAL INST

AI summary

A method of designing a robust integrated circuit that is not vulnerable to optical fault injection comprises training a variational autoencoder to identify regions in a target integrated circuit that are vulnerable to optical fault injection and altering the design of the target integrated circuit by altering the design of the vulnerable regions so that the target integrated circuit is no longer vulnerable to optical fault injection, thereby forming the robust integrated circuit.