Abstract

The increasing complexity of automotive electronic systems necessitates robust diagnostic strategies to detect and mitigate latent faults effectively. Design-for-Test (DFT) methodologies, originally developed for manufacturing test, can be leveraged to enhance in-field diagnostics. This white paper explores strategies to utilize existing DFT and system architecture for latent fault detection, including test time optimizations through key-on/key-off strategies and real-time, on-the-go diagnostics.

Introduction

ISO 26262 mandates the detection and handling of latent faults to ensure functional safety in automotive systems. Traditional Built-In Self-Test (BIST) and other DFT methodologies primarily focus on production testing but can be extended to address in-field diagnostics. This paper outlines methodologies for leveraging existing DFT/architecture for safety-critical applications.

Challenges in Latent Fault Coverage

1. Limited In-Field Testing Capabilities: Traditional production test structures are not optimized for in-field applications.
2. Test Time Constraints: Testing during key-on and key-off phases must balance thorough coverage with startup time limitations.
3. Resource Utilization: Reusing DFT structures without impacting normal operation requires architectural considerations.
4. On-the-Go Diagnostics: Implementing runtime monitoring without excessive performance overhead.

Strategies for Latent Fault Detection

1. Reusing DFT for In-Field Tests

• Utilize existing scan chains for periodic in-field testing.
• Leverage BIST mechanisms for periodic self-checks.
• Enable memory BIST (MBIST) execution during non-critical operations.

2. Optimizing Test Time Using Key-On/Key-Off Strategies

• Execute quick, high-priority tests during the key-on phase.
• Perform extended self-tests during the key-off phase.
• Utilize predictive analytics to prioritize test execution based on failure history.

3. On-the-Go Diagnostics for Continuous Monitoring

• Integrate built-in error detection mechanisms within processing pipelines.
• Utilize software-based test pattern execution during idle cycles.
• Leverage hardware performance monitors for anomaly detection.

4. Adaptive Test Scheduling and Fault Injection

• Implement dynamic test scheduling based on operating conditions.
• Use fault injection mechanisms to validate diagnostic coverage.
• Adapt test strategies based on real-time system performance.

Implementation Considerations

1. DFT-Aware System Architecture: Ensure design supports test execution without impacting functional performance.
2. Efficient Power Management: Minimize energy consumption during test phases.
3. Scalability: Ensure methodologies adapt to increasing system complexity.
4. Compliance with Safety Standards: Validate strategies against ISO 26262 requirements.

Conclusion

By leveraging existing DFT methodologies, automotive systems can achieve improved latent fault coverage while optimizing test execution times. Implementing key-on/key-off strategies, on-the-go diagnostics, and adaptive test scheduling enhances safety and reliability without significant hardware modifications. Future advancements in AI-driven diagnostics and predictive maintenance will further refine these approaches.