SLOA294A June   2020  – April 2024 TPS3851-Q1 , TPS7A16A-Q1

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1 Introduction
  5. 2 Types of Faults and Quantitative Random Hardware Failure Metrics
  6. 3 Random Failures Over a Product Lifetime and Estimation of BFR
  7. 4 BFR Estimation Techniques
  8. 5 Siemens SN 29500 FIT model
  9. 6 IEC TR 62380
  10. 7 Recommended Assumptions for BFR Calculations
  11. 8 Special Considerations for Transient Faults
  12. 9 BFR Differences (Due to Package) Between IEC TR 62380 and SN 29500
  13. 10Effect of Power-on Hours on BFR
  14. 11What Can You Expect for TI Products
  15. 12Summary
  16. 13References
  17. 14Revision History

1 Introduction

Base failure rates (BFR) quantify the intrinsic reliability of the semiconductor component while operating under normal environmental conditions. BFR is typically multiplied by factors such as temperature, voltage and number of operating hours to arrive at a quantitative measure of the quality of the component.

One of the primary inputs for calculating random hardware metrics (as required by functional safety standards) is the BFR. It can be estimated by a variety of techniques. BFR estimation techniques rely on assumptions of failure modes; thus, differences in these underlying assumptions lead to differences in BFR estimations.

This paper focuses on two widely accepted techniques to estimate the BFR for semiconductor components; estimates per IEC Technical Report 62380(3) and SN 29500(4) respectively. BFR estimation is foundational to calculate quantitative random hardware metrics, including: and IEC

  • Safe failure fraction (SFF)
  • Probability of failure per hour (PFH) in high-demand mode; or probability of failure per day (PFD) in low demand mode
  • Single-point fault metric (SPFM)
  • Latent fault metric (LFM)
  • Probabilistic metric for random hardware failure (PMHF)

This paper also outlines factors that influence BFR and compares and contrasts the various techniques.