Self-healing hardware systems represent a transformative advancement in fault tolerance, enabling devices to autonomously detect, diagnose, and recover from malfunctions to ensure continuous and reliable operation. This paper explores the critical role of fault detection techniques in self-healing systems, focusing on methods such as built-in self-test (BIST), redundancy, and machine learning-based approaches. These techniques facilitate proactive and real-time fault resolution, addressing the limitations of traditional reactive strategies. The study highlights the challenges of achieving high detection accuracy, system scalability, and minimizing recovery time while examining advanced fault simulation methods like concurrent, differential, and statistical fault simulation. The increasing complexity of modern hardware systems, driven by the integration of powerful processors and intricate architectures, has made them more susceptible to failures caused by aging, environmental factors (e.g., temperature, radiation), and real-time task execution. Self-healing systems leverage fault tolerance, redundancy, and dynamic reconfiguration to autonomously recover from damage, preserving functionality and minimizing disruption. This paper provides a comprehensive overview of fault detection and repair methodologies, emphasizing their role in enhancing system robustness and long-term resilience. Key techniques such as real-time fault monitoring, hardware redundancy, and AI/ML-based fault prediction are discussed, alongside fault injection and self-repair mechanisms. The paper also addresses fault modeling, including single-fault and multiple-fault models, and their computational challenges. By integrating fault detection with self-repair strategies, self-healing systems offer a robust solution for maintaining reliability in critical applications such as aerospace, automotive, and healthcare. This work underscores the transformative potential of self-healing technologies, paving the way for resilient and adaptive hardware architectures capable of thriving in demanding operational environments.

Keywords: Self-healing hardware, fault tolerance, fault detection, built-in self-test (BIST), redundancy, machine learning, fault simulation, fault models, real-time monitoring, dynamic reconfiguration.

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1. Khalil K, Eldash O, Kumar A, Bayoumi M. Self-healing approach for hardware neural network architecture. In2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 2019 Aug 4 (pp. 622-625). IEEE.
2. Medeiros GC. Test and diagnosis of hard-to-detect faults in finfet srams.
3. Thakur S, Satyanarayana KV, Reddy KC. Diagnosis of parametric faults in linear analog VLSI circuits. In2016 10th International Conference on Intelligent Systems and Control (ISCO) 2016 Jan 7 (pp. 1-5). IEEE.
4. Frei R, McWilliam R, Derrick B, Purvis A, Tiwari A, Di Marzo Serugendo G. Self-healing and self-repairing technologies. The International Journal of Advanced Manufacturing Technology. 2013 Nov;69:1033-61.
5. Pateras S. Embedded diagnosis IP. InProceedings 2002 Design, Automation and Test in Europe Conference and Exhibition 2002 Mar 4 (pp. 242-243). IEEE.
6. Ubar R, Kostin S, Raik J. Embedded fault diagnosis in digital systems with BIST. Microprocessors and Microsystems. 2008 Aug 1;32(5-6):279-87.
7. Clouqueur T, Ercevik O, Saluja KK. Efficient signature-based fault diagnosis using variable size windows. InVLSI Design 2001. Fourteenth International Conference on VLSI Design 2001 Jan 7 (pp. 391-396). IEEE.
8. Liu C, Chakrabarty K. Failing vector identification based on overlapping intervals of test vectors in a scan-BIST environment. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2003 Apr 29;22(5):593-604.
9. Bartenstein T, Heaberlin D, Huisman L, Sliwinski D. Diagnosing combinational logic designs using the single location at-a-time (SLAT) paradigm. InProceedings International Test Conference 2001 (Cat. No. 01CH37260) 2001 Nov 1 (pp. 287-296). IEEE.
10. Seaton J. Fault simulation basics. InProceedings., Second Annual IEEE ASIC Seminar and Exhibit, 1989 Sep 25 (pp. T5-1). IEEE.
11. Zhang Y, Agrawal VD. An algorithm for diagnostic fault simulation. In2010 11th Latin American Test Workshop 2010 Mar 28 (pp. 1-5). IEEE.
12. Levendel YH, Menon PR. Fault‐Simulation Methods—Extensions and Comparison. Bell System Technical Journal. 1981 Nov;60(9):2235-58.

Self-healing hardware systems represent a transformative advancement in fault tolerance, enabling devices to autonomously detect, diagnose, and recover from malfunctions to ensure continuous and reliable operation. This paper explores the critical role of fault detection techniques in self-healing systems, focusing on methods such as built-in self-test (BIST), redundancy, and machine learning-based approaches. These techniques facilitate proactive and real-time fault resolution, addressing the limitations of traditional reactive strategies. The study highlights the challenges of achieving high detection accuracy, system scalability, and minimizing recovery time while examining advanced fault simulation methods like concurrent, differential, and statistical fault simulation. The increasing complexity of modern hardware systems, driven by the integration of powerful processors and intricate architectures, has made them more susceptible to failures caused by aging, environmental factors (e.g., temperature, radiation), and real-time task execution. Self-healing systems leverage fault tolerance, redundancy, and dynamic reconfiguration to autonomously recover from damage, preserving functionality and minimizing disruption. This paper provides a comprehensive overview of fault detection and repair methodologies, emphasizing their role in enhancing system robustness and long-term resilience. Key techniques such as real-time fault monitoring, hardware redundancy, and AI/ML-based fault prediction are discussed, alongside fault injection and self-repair mechanisms. The paper also addresses fault modeling, including single-fault and multiple-fault models, and their computational challenges. By integrating fault detection with self-repair strategies, self-healing systems offer a robust solution for maintaining reliability in critical applications such as aerospace, automotive, and healthcare. This work underscores the transformative potential of self-healing technologies, paving the way for resilient and adaptive hardware architectures capable of thriving in demanding operational environments.

Keywords: Self-healing hardware, fault tolerance, fault detection, built-in self-test (BIST), redundancy, machine learning, fault simulation, fault models, real-time monitoring, dynamic reconfiguration.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Khalil K, Eldash O, Kumar A, Bayoumi M. Self-healing approach for hardware neural network architecture. In2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 2019 Aug 4 (pp. 622-625). IEEE.
2. Medeiros GC. Test and diagnosis of hard-to-detect faults in finfet srams.
3. Thakur S, Satyanarayana KV, Reddy KC. Diagnosis of parametric faults in linear analog VLSI circuits. In2016 10th International Conference on Intelligent Systems and Control (ISCO) 2016 Jan 7 (pp. 1-5). IEEE.
4. Frei R, McWilliam R, Derrick B, Purvis A, Tiwari A, Di Marzo Serugendo G. Self-healing and self-repairing technologies. The International Journal of Advanced Manufacturing Technology. 2013 Nov;69:1033-61.
5. Pateras S. Embedded diagnosis IP. InProceedings 2002 Design, Automation and Test in Europe Conference and Exhibition 2002 Mar 4 (pp. 242-243). IEEE.
6. Ubar R, Kostin S, Raik J. Embedded fault diagnosis in digital systems with BIST. Microprocessors and Microsystems. 2008 Aug 1;32(5-6):279-87.
7. Clouqueur T, Ercevik O, Saluja KK. Efficient signature-based fault diagnosis using variable size windows. InVLSI Design 2001. Fourteenth International Conference on VLSI Design 2001 Jan 7 (pp. 391-396). IEEE.
8. Liu C, Chakrabarty K. Failing vector identification based on overlapping intervals of test vectors in a scan-BIST environment. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2003 Apr 29;22(5):593-604.
9. Bartenstein T, Heaberlin D, Huisman L, Sliwinski D. Diagnosing combinational logic designs using the single location at-a-time (SLAT) paradigm. InProceedings International Test Conference 2001 (Cat. No. 01CH37260) 2001 Nov 1 (pp. 287-296). IEEE.
10. Seaton J. Fault simulation basics. InProceedings., Second Annual IEEE ASIC Seminar and Exhibit, 1989 Sep 25 (pp. T5-1). IEEE.
11. Zhang Y, Agrawal VD. An algorithm for diagnostic fault simulation. In2010 11th Latin American Test Workshop 2010 Mar 28 (pp. 1-5). IEEE.
12. Levendel YH, Menon PR. Fault‐Simulation Methods—Extensions and Comparison. Bell System Technical Journal. 1981 Nov;60(9):2235-58.