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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA “bathtub curve” can mean two different engineering plots. In reliability engineering, it shows a population’s failure rate over product age. In signal integrity, it shows bit- or symbol-error rate across sampling time within a unit interval, revealing timing margin at a chosen error-rate target. The curves share a bathtub-like shape, but their axes, data, and uses are different.
What is a bathtub curve?
The meaning depends on the engineering context. A reliability bathtub curve relates failure rate to age; a signal-integrity bathtub curve relates BER or SER to timing position. NIST describes the reliability plot as a familiar empirical pattern, while Ansys documents the signal-integrity plot as an eye-related measure. Neither plot should be interpreted without checking its axes.
| Context | Horizontal axis | Vertical axis | What it helps answer |
|---|---|---|---|
| Reliability engineering | Product or population age / time | Failure rate; for repairable systems, repair rate or rate of occurrence of failures (ROCOF) may be used | How does the observed failure or repair pattern change over time? |
| Signal integrity | Sampling time or phase across a unit interval | Estimated bit error rate (BER), or supported symbol error rate (SER) | How much timing opening is available at a specified error-rate target? |
The reliability definition and repairable-system note are from the NIST/SEMATECH Engineering Statistics Handbook. The signal-integrity definition and measurement-point examples are in Ansys AMI documentation.
What does a bathtub curve show in reliability engineering?
For a population of products, the conventional curve has three regions: an initially declining failure rate, a roughly stable interval, and a later increase associated with wearout. NIST notes that the pattern has been observed across different mechanical and electronic components and systems, but it is an empirical model—not a guarantee that every product or dataset follows all three stages.
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Early failures
The failure rate is initially high and declines as early defects appear or are removed. This is often called the infant-mortality period.
Stable or useful-life period
The rate is approximately level. “Stable” describes the observed rate over this region; it does not mean that individual units cannot fail.
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Wearout
The rate rises as materials degrade and wear-related failures become more likely. The curve does not establish a universal age at which wearout begins; that depends on the product, population, operating conditions, and observed failure modes.
NIST summarizes the familiar shape with the sentence: “A plot of the failure rate over time for most products yields a curve that looks like a drawing of a bathtub.” The handbook’s section on the “Bathtub” curve also describes an interval-based empirical rate estimate: failures during an interval divided by the number of units surviving to the interval’s start and the interval duration. Its 13th-month illustration uses r13/(N12 × 720 hours); that is an example of the calculation, not a general product statistic.
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How is a bathtub curve used with an eye diagram?
For a serial link, the plot traces estimated BER—or SER where the signaling and analysis support it—against sampling time or phase across one unit interval. At a selected error-rate level, the horizontal span of the curve represents the timing opening available at that target. It is a timing-margin view of a link, not a component’s failure rate over service life.
An eye characterization can be used to estimate BER and construct a bathtub curve. TI describes workflows in which engineers characterize around BER levels of 10-6 to 10-9 and extrapolate toward 10-12 or beyond; those are examples of practice, not requirements for every interface or standard. See TI Precision Labs’ eye-diagram material.
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Measured data and extrapolated tails
Directly observing enough errors to establish an extremely low BER can take a long time. As a result, a plotted low-BER tail may be projected from a smaller sample using a mathematical model rather than measured directly. Tektronix explains: “Therefore, mathematical models discussed in Chapter 4 are used to predict performance based on much smaller sample sets.” Its jitter and timing fundamentals material includes an example based on 42,000 observed edges; the inferred eye openings at BER 1e-12 differ between two systems. That example illustrates why finite-sample jitter summaries alone do not determine low-BER margin; it is not a general performance result.
When reading a signal-integrity curve, distinguish the region supported by observations from the region estimated by a model. Ansys documentation for version 26.1 describes reporting at the initial eye, transmitter output, channel input, and receiver output, and notes modulation-specific support, including differences for PAM3/PAM4. It also marks a simulated limit and warns that extrapolation can be unreliable when TX random jitter is present and the simulated-bit count is below 2.5e5. This is an Ansys interface condition, not an industry-wide minimum.
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How should you compare bathtub curves?
Compare reliability curves
- Confirm the population and how age or time is measured.
- Check which failures and failure modes are counted, along with the operating environment and observation period.
- Verify whether the vertical axis is failure rate or, for a repairable system, repair rate/ROCOF.
- Do not infer a universal service-life transition from a generic bathtub sketch.
Compare signal-integrity curves
- Use the same BER or SER target and compare the horizontal timing opening at that target.
- Match modulation and, for PAM signaling, the relevant sub-eye.
- Check where in the transmitter/channel/receiver path each curve was produced.
- Ask how many samples or simulated bits support the result, which tail segments are measured versus extrapolated, and what model assumptions apply.
A low-BER margin comparison is meaningful only when the target and measurement or modeling conditions are compatible. A plotted curve without its axis definitions, target level, population or sample basis, and method can look precise while answering a different question than the one you have.
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