Electronics Guide

HALT Fundamentals

HALT differs fundamentally from traditional qualification testing:

Design improvement focus: HALT is not a pass/fail test but a discovery tool. The objective is to find design weaknesses and operating limits, not to verify compliance with a specification. Every failure found represents an opportunity for design improvement.

Step-stress approach: Stresses are increased in steps until failures occur. Each failure is analyzed, corrective action is implemented if practical, and testing continues to find additional weaknesses. The process continues until fundamental technology limits are reached or practical limits of the test equipment.

Combined stresses: HALT typically combines temperature cycling, vibration, and sometimes voltage or frequency variations. The combination of stresses often reveals failures at lower individual stress levels than single-stress testing.

Rapid thermal transitions: HALT chambers use liquid nitrogen or other means to achieve temperature change rates of 40-60 degrees Celsius per minute, far exceeding natural thermal transitions. This rapid cycling accelerates thermal fatigue mechanisms.

EMC Integration in HALT

Integrating EMC assessment into HALT requires adaptation of both procedures:

Baseline EMC characterization: Before stress application, complete EMC measurements establish the starting point. These measurements provide reference for detecting degradation during HALT and help identify which parameters are most vulnerable.

Continuous monitoring during stress: Where practical, continuous electrical monitoring during stress can detect transient EMC failures that might self-heal when stress is removed. This is simpler for conducted measurements than radiated measurements.

Periodic EMC testing: Full EMC testing at intervals during the HALT sequence captures progressive degradation. Testing should occur at consistent conditions (typically room temperature) to isolate stress-induced degradation from temperature-dependent performance variation.

Failure mode EMC analysis: When HALT precipitates failures, EMC testing can determine whether failures manifest as EMC problems (increased emissions, reduced immunity) in addition to or instead of functional failures. Some EMC failures occur before functional failure.

Operating Limit Identification

HALT reveals multiple types of operating limits:

Lower operating limit (LOL): The cold temperature at which the product first exhibits functional failures or EMC non-compliance. EMC limits may be reached at higher temperatures than functional limits if components affecting EMC behavior are more temperature-sensitive.

Upper operating limit (UOL): The high temperature at which failures first occur. Again, EMC limits may differ from functional limits. High-temperature EMC failures often involve degraded filtering (ceramic capacitor value drop) or increased emissions (faster switching speeds).

Vibration limit: The vibration level at which failures occur. EMC-related vibration failures typically involve intermittent connections in shielded connectors, fractured solder joints on filter components, or mechanical resonance affecting sensitive crystals.

Destruct limits: Beyond operating limits, higher stress levels reveal destruct limits where permanent damage occurs. The margin between operating and destruct limits indicates design robustness.

Interpreting HALT Results

HALT results require careful interpretation for EMC applications:

The stresses applied in HALT typically far exceed specification limits. Failures at extreme conditions do not necessarily indicate field problems. However, the margin between operating limits and specification limits indicates the robustness of the design.

Failures found during HALT represent opportunities for design improvement, not automatic design rejections. The decision to implement corrective action depends on the margin available, the cost of correction, and the consequences of field failure.

EMC margins discovered through HALT inform risk assessment for new product introduction. Products with large EMC margins at HALT limits are more likely to maintain compliance throughout service life. Products with marginal EMC performance may require monitoring programs or design changes.

Correlation between HALT results and field performance improves with experience. Initial HALT programs may over- or under-predict field issues until the relationship between HALT stresses and field stresses is calibrated for specific product types and applications.

HASS Fundamentals

HASS is a production screening process derived from HALT findings:

Defect precipitation: HASS applies stress sufficient to precipitate defects that would cause early field failures, but not enough to damage properly manufactured products. The stress profile is developed based on HALT results and refined based on production experience.

Stress levels: HASS stresses are typically 20-50% of the operating limits found in HALT. This provides sufficient acceleration to precipitate defects in reasonable test times while maintaining acceptable yield impact on good product.

Production integration: HASS must be practical for production volumes. Test times of 2-8 hours are typical, compared to days for HALT. Automated test equipment and efficient loading/unloading are essential.

Proof of screen: The effectiveness of HASS is verified by periodically testing product through HALT after HASS. If HASS is effective, post-HASS HALT should show higher operating limits than pre-HASS HALT, indicating that weak units have been screened out.

EMC in Production Screening

Including EMC in HASS addresses manufacturing variations affecting electromagnetic performance:

Screening criteria: EMC measurements during or after HASS provide additional screening criteria beyond functional test. Products that pass functional test but show degraded EMC performance may have defects that will cause field problems.

Marginal solder joints: Cold or fractured solder joints on filter components, grounding straps, or shielding connections may pass functional test but affect EMC performance. HASS stress precipitates these marginal joints to detectable failures.

Component defects: Marginal components (out-of-tolerance capacitors, weak ferrite cores, contaminated contacts) may function but provide inadequate filtering or shielding. EMC testing after HASS detects units with marginal components.

Assembly variations: Inconsistent assembly of shielding components, improper connector torque, or missing components can affect EMC performance. HASS stress may precipitate failures in improperly assembled units.

HASS Profile Development

Developing effective HASS profiles requires balancing multiple considerations:

Defect coverage: The profile must apply sufficient stress to precipitate the defect types of concern. Temperature cycling precipitates solder joint defects; vibration reveals loose connections; combined stress addresses both.

Product protection: Excessive stress damages good product, reducing yield. The profile must remain below the operating limits of compliant product. Margins are necessary to accommodate measurement uncertainty and product variation.

Test time: Practical production screening requires reasonable test times. Longer tests may improve defect detection but reduce throughput. Optimization balances detection probability against production impact.

EMC test integration: Full EMC testing in a production environment is challenging. Often, simplified parametric measurements (filter insertion loss, shield continuity, return loss) serve as proxies for full EMC performance. These measurements must correlate with actual EMC performance based on characterization data.

Manufacturing Feedback

HASS results provide valuable manufacturing process feedback:

Analysis of HASS failures reveals patterns that indicate systematic manufacturing problems. Clusters of failures at specific assemblies or time periods point to process issues. Root cause analysis of HASS failures guides process improvement.

EMC-related HASS failures may indicate issues not visible in functional test data. A trend of degraded filter performance after HASS might reveal soldering process issues with filter components. Intermittent shielding failures might indicate connector assembly problems.

Statistical process control of HASS results provides early warning of manufacturing drift. Trending of EMC parameters through HASS can detect gradual process changes before they cause field problems.

Feedback loops between HASS, manufacturing, and design enable continuous improvement. Recurring HASS failures drive design changes that make products inherently more manufacturable and robust.

Step-Stress Methodology

Step-stress tests follow a structured approach:

Stress selection: The stress variable is selected based on the expected degradation mechanism. Temperature is most common, but vibration, humidity, voltage, or combinations may be appropriate depending on the failure modes of concern.

Step size and duration: Stress is increased in discrete steps, with sufficient time at each step to allow degradation to occur and be measured. Larger steps provide faster testing but coarser resolution; smaller steps improve resolution but increase test time.

Measurement intervals: Performance is measured at defined intervals during each stress step. For EMC applications, measurements may include emissions levels, immunity thresholds, and filter or shield performance parameters.

Failure criteria: Failure may be defined as exceeding a specification limit, degrading by a percentage from initial performance, or losing functionality. EMC failure criteria should be defined in advance and may include both absolute limits and relative degradation limits.

EMC Parameter Monitoring

Effective step-stress testing requires identification of critical EMC parameters:

Emissions parameters: Peak emission levels at critical frequencies, total radiated power, or worst-case margin to limits can be monitored. Changes in emission patterns (new peaks, frequency shifts) may indicate component degradation.

Immunity thresholds: The stress level at which immunity failures first occur characterizes susceptibility margin. Degradation appears as reduced stress tolerance at each step-stress level.

Component parameters: Direct measurement of filter insertion loss, shield effectiveness, or decoupling capacitor impedance provides insight into component degradation. These measurements are often simpler than full EMC testing.

Functional parameters: Signal integrity metrics (jitter, bit error rate, signal-to-noise ratio) often correlate with EMC performance and can be monitored more easily in production environments.

Data Analysis

Step-stress data supports multiple analyses:

Degradation curves: Plotting parameter values against stress level and time reveals degradation patterns. Linear, exponential, and threshold behaviors indicate different degradation mechanisms.

Operating limit identification: The stress level at which parameters first exceed limits defines the operating limit. Margins at specification stress levels indicate robustness.

Acceleration factor extraction: If the same failure mode can be induced at different stress levels, the ratio of times to failure provides acceleration factor data for life modeling.

Mechanism identification: The pattern of degradation often indicates the underlying mechanism. Temperature-activated mechanisms show Arrhenius behavior; fatigue mechanisms show power-law cycle dependence; corrosion mechanisms show time-dependent progression.

Practical Considerations

Implementing step-stress testing requires attention to practical details:

Sample size: Statistical confidence requires multiple samples. For screening purposes, a few samples may suffice; for life prediction, larger samples improve confidence in acceleration factors.

Measurement accuracy: Detecting gradual degradation requires measurement systems with accuracy better than the changes being detected. Calibration and repeatability verification are essential.

Environmental control: Consistent measurement conditions are necessary for valid comparisons. Temperature-dependent parameters should be measured at controlled temperature, even when stress testing at elevated temperatures.

Data management: Step-stress testing generates substantial data. Systematic data collection, storage, and analysis support meaningful conclusions and future reference.

Degradation Model Types

Several model forms describe common degradation patterns:

Linear degradation: Parameters change at a constant rate over time or cycles: D(t) = D0 + kt, where D is the degradation measure, D0 is the initial value, k is the degradation rate, and t is time. Linear models are simplest but often inadequate for complex systems.

Power law degradation: Degradation follows a power relationship with time or cycles: D(t) = D0 + At^n. Many fatigue and wear mechanisms follow power laws. The exponent n characterizes the degradation acceleration.

Exponential degradation: Degradation accelerates exponentially: D(t) = D0 * exp(kt). This form describes mechanisms with positive feedback, where degradation creates conditions that accelerate further degradation.

Threshold models: Some mechanisms remain dormant until a threshold condition is reached, then progress rapidly. Corrosion penetrating a protective coating or fatigue cracks reaching critical length exhibit threshold behavior.

EMC-Specific Degradation

Different EMC components follow characteristic degradation patterns:

Filter capacitor degradation: Electrolytic capacitors lose capacitance and develop increased ESR over time and temperature. The degradation typically follows Arrhenius temperature dependence with time, allowing life prediction from accelerated high-temperature testing.

Ferrite core degradation: Ferrite permeability can degrade from thermal shock, mechanical stress, or chemical attack. Degradation often appears as a gradual reduction in filter effectiveness at lower frequencies.

Shield effectiveness degradation: Corrosion, gasket compression set, and connector wear gradually degrade shielding. The degradation may follow threshold behavior, with little change until a critical condition is reached.

Solder joint degradation: Thermal fatigue causes gradual crack propagation in solder joints. Resistance increases gradually until intermittent behavior begins, often followed by rapid progression to failure.

Model Fitting and Validation

Developing valid degradation models requires systematic approaches:

Data collection: Sufficient data points across the degradation range are necessary for meaningful model fitting. Both accelerated test data and field return data, when available, inform model development.

Model selection: The appropriate model form depends on the underlying mechanism. Physical understanding guides initial model selection; statistical fit measures confirm appropriateness.

Parameter estimation: Model parameters are estimated from test data using regression or maximum likelihood methods. Confidence intervals on parameters propagate to uncertainty in predictions.

Validation: Models should be validated against independent data not used in fitting. Field data, when available, provides the ultimate validation. Predictions outside the range of test data should be viewed with appropriate caution.

Predictive Applications

Degradation models enable several predictive applications:

Remaining useful life: Given current degradation state, models predict time until parameters exceed limits. This enables condition-based maintenance strategies.

Warranty exposure: Predicting the fraction of products that will degrade beyond limits during the warranty period informs warranty cost estimation and policy decisions.

Design margin requirements: Knowing how much degradation to expect over service life, designers can specify initial margins that ensure end-of-life compliance.

Maintenance intervals: For repairable or adjustable parameters, degradation models inform optimal maintenance intervals that balance service cost against failure risk.

Arrhenius Acceleration

Temperature-accelerated testing commonly uses the Arrhenius model:

The Arrhenius equation describes the temperature dependence of reaction rates: Rate = A * exp(-Ea/kT), where A is a constant, Ea is activation energy, k is Boltzmann's constant, and T is absolute temperature.

The acceleration factor between test temperature and use temperature is: AF = exp[(Ea/k) * (1/Tuse - 1/Ttest)]

Activation energy Ea characterizes the failure mechanism. Different mechanisms have different activation energies: semiconductor failure mechanisms typically show Ea of 0.5-1.2 eV; corrosion mechanisms may show 0.4-0.8 eV; electrolytic capacitor wear shows approximately 0.4 eV.

Using incorrect activation energy leads to incorrect life predictions. Mechanism identification is essential for valid acceleration. If the accelerated test induces a different failure mode than field operation, the acceleration factor is meaningless.

Coffin-Manson for Thermal Cycling

Thermal fatigue follows the Coffin-Manson relationship:

The number of cycles to failure relates to the temperature range: Nf = C * (delta_T)^(-n), where Nf is cycles to failure, delta_T is the temperature range, and n is the fatigue exponent (typically 1.9-2.5 for solder joints).

The acceleration factor for thermal cycling is: AF = (delta_T_test / delta_T_use)^n

This model assumes the failure mechanism is the same at both temperature ranges. If the accelerated test range induces plastic deformation while use conditions cause only elastic stress, the acceleration model is invalid.

More sophisticated models (Norris-Landzberg, Engelmaier) include additional factors such as cycle frequency and mean temperature, providing more accurate predictions for complex temperature profiles.

Humidity Acceleration

Humidity-accelerated testing uses models specific to moisture-related mechanisms:

The Peck model combines temperature and humidity effects: AF = (RH_test/RH_use)^n * exp[(Ea/k) * (1/Tuse - 1/Ttest)], where RH is relative humidity and n is typically 2.5-3 for moisture-related mechanisms.

Humidity acceleration assumes that the same mechanism operates at both humidity levels. At very high humidity, condensation may introduce mechanisms not present at lower humidity. At very low humidity, some moisture-dependent mechanisms may not operate at all.

Time to absorb moisture to equilibrium must be considered. Short test durations at high humidity may not allow sufficient moisture penetration to activate internal degradation mechanisms.

Vibration Acceleration

Vibration fatigue acceleration uses power law relationships:

For vibration fatigue, the relationship is: t_equivalent = t_test * (g_test/g_use)^b, where g is the acceleration level and b is an exponent typically in the range of 4-8 for electronic assemblies.

The high exponent values mean that moderate increases in vibration level provide substantial acceleration. However, excessive vibration may induce failure modes (such as connector unmating) not relevant to normal operation.

Vibration acceleration assumes that the fatigue mechanism is the same at both levels. If high-level testing induces different stress distributions or excites different resonances than use conditions, the acceleration relationship may not hold.

Combined Acceleration

When multiple stresses are applied simultaneously, acceleration effects may multiply:

For independent mechanisms, the combined acceleration factor is the product of individual acceleration factors: AF_combined = AF_temp * AF_humidity * AF_vibration

Synergistic interactions may produce greater acceleration than the product would suggest. Temperature and humidity together may accelerate corrosion more than either alone. Vibration and temperature together may accelerate thermal fatigue through enhanced crack propagation.

Antagonistic interactions are also possible. High temperature may dry materials, reducing humidity effects. Very high vibration may work-harden materials, increasing fatigue resistance.

Combined stress acceleration requires experimental validation for specific product types and failure mechanisms. Generic multiplication of individual acceleration factors may significantly over- or under-predict combined acceleration.

Initial Margin Characterization

Understanding margin erosion begins with quantifying initial margins:

Emissions margin: The difference between measured emissions and the limit, in dB, represents the margin available for degradation. Products with 6 dB margin can tolerate a doubling of emissions before exceeding limits; products with 20 dB margin can tolerate a factor of 10 increase.

Immunity margin: The ratio of the stress level causing first failure to the specification test level indicates immunity margin. A product that fails at 150% of the test level has 50% margin; one that fails at the test level has no margin.

Worst-case analysis: Initial margins should account for measurement uncertainty, production variation, and temperature effects. A measured margin of 6 dB may provide only 2-3 dB of true margin after uncertainties are considered.

Critical parameters: Not all parameters erode equally. Identifying which parameters have the smallest initial margins and the highest degradation rates focuses attention on the limiting factors.

Erosion Mechanisms

Various mechanisms cause margin erosion over product life:

Component aging: Capacitors lose capacitance and develop higher ESR over time. Inductors may degrade if cores are exposed to stress or contamination. Resistors generally are stable, but some types drift under environmental stress.

Mechanical degradation: Gasket compression set reduces shielding effectiveness over time. Connector contacts oxidize and develop higher resistance. Solder joints fatigue from thermal cycling.

Environmental effects: Corrosion progressively degrades metal surfaces and connections. Moisture absorption changes dielectric properties. UV exposure degrades plastics and coatings.

Wear mechanisms: Repeated operation causes wear in mechanical components such as switches, connectors, and fans. Wear affects both function and EMC-relevant parameters like contact resistance and motor brush noise.

Margin Tracking

Systematic tracking of margins supports life management:

Production tracking: Recording EMC performance of production units establishes the initial margin distribution. Statistical characterization reveals typical margins and identifies low-margin units.

Accelerated aging correlation: Comparing accelerated test results with initial margins shows how margins erode under stress. This correlation enables prediction of field margin erosion.

Field monitoring: When practical, field monitoring of EMC-relevant parameters provides direct evidence of margin erosion in actual use conditions. This validates or corrects predictions from accelerated testing.

Fleet management: For product fleets, margin tracking enables prediction of when units will approach compliance limits. Proactive maintenance or replacement can be scheduled before problems occur.

Margin Management Strategies

Several strategies address margin erosion:

Design for margin: Initial designs should include explicit margin requirements beyond specification limits, sized to accommodate expected degradation plus uncertainty. Margin budgets should allocate margin to each contributing factor.

Component derating: Using components at stress levels below their ratings reduces degradation rates and extends life. Filter capacitors rated for 125 degrees C but operated at 85 degrees C, for example, will maintain performance much longer.

Redundancy: Critical functions may use redundant components so that degradation of one does not immediately cause compliance failure. Parallel capacitors in filters, for instance, provide margin against single-capacitor degradation.

Maintenance provisions: Replaceable components, accessible adjustment points, and monitoring provisions enable periodic restoration of margins. This approach trades operating cost against capital cost of higher initial margins.

Drift Characterization

Understanding drift patterns supports prediction:

Systematic drift: Some parameters drift in predictable directions. Electrolytic capacitor ESR increases over time; ceramic capacitor value may decrease. These systematic trends can be extrapolated.

Random drift: Other parameters may drift in either direction depending on specific conditions. Crystal oscillator frequency may increase or decrease depending on aging mechanisms. Random drift is harder to predict for individual units.

Drift rate variation: Drift rates vary between units based on initial component selection, manufacturing variations, and operating conditions. Population statistics characterize the distribution of drift rates.

Non-linear drift: Some parameters drift non-linearly, perhaps accelerating as degradation progresses. Models must capture this non-linearity for accurate long-term prediction.

Prediction Methods

Several methods support drift prediction:

Extrapolation: Given measurements at multiple times, trends can be extrapolated to predict future values. Simple linear extrapolation works for linear drift; more sophisticated methods handle non-linear patterns.

Physics-based models: When the degradation mechanism is understood, physics-based models predict drift based on operating conditions. These models can predict performance under conditions not directly tested.

Statistical models: Population-based statistical models predict the distribution of future values based on initial distributions and degradation characteristics. These models support fleet-level predictions.

Machine learning: Data-driven approaches can learn complex degradation patterns from large datasets, potentially capturing interactions not included in physics-based models. However, they require substantial training data and may not extrapolate well beyond the training range.

Uncertainty Quantification

Predictions must include uncertainty estimates:

Measurement uncertainty: Uncertainty in initial measurements propagates to uncertainty in predictions. More precise initial measurements improve prediction accuracy.

Model uncertainty: Degradation models are approximations of reality. Model selection and parameter estimation both contribute uncertainty. Comparing predictions from multiple models helps assess model uncertainty.

Environmental uncertainty: Future operating conditions may differ from assumptions. Sensitivity analysis shows how predictions change with operating condition assumptions.

Confidence intervals: Predictions should be expressed as probability distributions or confidence intervals rather than single values. Decision-makers can then assess risk based on the probability of exceeding limits.

Applications of Drift Prediction

Drift predictions support multiple decisions:

End-of-life prediction: Predicting when parameters will exceed limits enables planning for replacement or refurbishment. Lead time for procurement and scheduling can be incorporated.

Condition-based maintenance: Rather than fixed maintenance intervals, maintenance can be scheduled based on predicted time to limit. This optimizes maintenance resources while managing failure risk.

Life extension assessment: When considering extending product life beyond original design life, drift prediction shows whether EMC margins will remain adequate for the extended period.

Design feedback: Comparison of predicted and actual drift provides feedback for design improvement. Parameters that drift faster than predicted indicate areas for design attention.

Warranty Failure Prediction

Predicting warranty failures requires several inputs:

Initial margin distribution: The distribution of initial margins in the production population determines how many units are near the failure threshold from the start.

Degradation rate distribution: The distribution of degradation rates determines how quickly the population approaches the failure threshold. High-rate units fail first.

Operating condition distribution: Different customers operate products under different conditions. Customers in harsh environments experience faster degradation.

Time to failure distribution: Combining initial margins, degradation rates, and operating conditions yields a distribution of times to failure. Integration over the warranty period gives the expected failure fraction.

Warranty Cost Modeling

EMC-related warranty costs include multiple elements:

Repair costs: The direct cost of diagnosing and repairing failed units includes labor, parts, and overhead. EMC failures may require specialized diagnosis capability.

Logistics costs: Shipping units for repair, providing loaners, or dispatching field service adds to warranty costs. These costs depend on product size, value, and service infrastructure.

Goodwill costs: Customer dissatisfaction from failures can affect future sales. Products with reputation for EMC problems may lose market share even if warranty coverage applies.

Regulatory costs: In some cases, EMC failures may trigger regulatory action if products no longer meet requirements. Recalls or corrective actions add significant costs.

Warranty Period Optimization

Warranty period decisions balance multiple factors:

Competitive position: Warranty period affects market positioning. Longer warranties signal confidence in product quality and may justify premium pricing.

Cost exposure: Longer warranties increase cost exposure, particularly for products with predictable wear-out mechanisms. The cost of extended warranty must be offset by price premium or competitive advantage.

Design feedback loop: Warranty periods should be long enough to capture degradation-related failures, providing feedback for design improvement. Very short warranties may hide degradation problems until out-of-warranty failures damage reputation.

EMC-specific considerations: EMC failures may be harder for customers to identify, potentially underreporting warranty claims. Alternatively, EMC problems may cause functional symptoms that result in warranty claims classified as other failure modes.

Extended Warranty Considerations

Extended warranties for EMC performance require special analysis:

Degradation mechanisms that cause gradual margin erosion become increasingly significant in extended warranty periods. The fraction of products failing increases non-linearly as more of the population reaches end-of-life.

Replacement parts may have different characteristics than original parts. If replacement components have different degradation behavior, extended warranty predictions must account for mixed populations.

Operating condition changes over product life may affect degradation. Products moved to harsher environments or operated at higher duty cycles than originally may fail sooner than predicted.

Extended warranty pricing should reflect the non-linear increase in failure probability. Simple linear extrapolation of short-term failure rates underestimates extended warranty costs.

Demonstration Test Design

Designing demonstration tests requires specifying several parameters:

Reliability requirement: The target reliability (e.g., 95% of units meet EMC requirements at end of life) defines the goal. Higher reliability requirements demand more extensive testing.

Confidence level: The statistical confidence (e.g., 90% confidence that 95% of units meet requirements) determines sample size and test duration. Higher confidence requires more testing.

Test duration: Accelerated tests are scaled to equivalent field duration using acceleration factors. The equivalent field duration must cover the intended product life.

Sample size: More samples provide higher statistical confidence. Sample size trades off against test cost and schedule. Zero-failure test plans require smaller samples than plans allowing some failures.

Zero-Failure vs. Failure-Based Plans

Two approaches to reliability demonstration have different characteristics:

Zero-failure plans: All test samples must pass to demonstrate reliability. Sample size and test duration are selected so that achieving zero failures provides the required confidence. Zero-failure plans are efficient when the product is actually highly reliable.

Failure-based plans: The test plan specifies an allowable number of failures and a sample size such that observing no more than the allowable failures demonstrates reliability. These plans can demonstrate reliability even when some failures occur and provide data on failure modes.

For EMC reliability demonstration, failure definition must be clear. Degradation below specification is a failure, but temporary excursions during stress transitions may or may not be counted depending on the requirement.

Censored data (samples removed from test before failure for other reasons) requires appropriate statistical handling. Early removal without failure provides partial evidence of reliability.

Accelerated Demonstration Tests

Practical demonstration tests use acceleration to compress test duration:

Acceleration factor validation: The acceleration factors used must be validated for the specific failure mechanisms of concern. Incorrect factors lead to incorrect reliability claims.

Failure mechanism preservation: Accelerated conditions must induce the same failure mechanisms as field conditions. If different mechanisms dominate at test conditions, the demonstration is not valid for field reliability.

Multiple mechanisms: Products may have multiple degradation mechanisms affecting EMC. The demonstration test must address all significant mechanisms, which may require multiple test conditions or combined stress testing.

Cumulative damage models: When products experience varying stress levels, cumulative damage models track damage accumulation from multiple stress profiles. Miner's rule and similar approaches sum damage fractions from different conditions.

Documentation and Reporting

Reliability demonstration requires comprehensive documentation:

Test conditions: Complete specification of stress levels, profiles, and durations enables assessment of test adequacy and replication if needed.

Acceleration factors: The basis for acceleration factors, whether from literature, testing, or physics models, must be documented. Uncertainty in acceleration factors affects confidence in demonstrated reliability.

EMC measurements: Complete EMC measurement data, including measurement uncertainty, supports the reliability claim. Both initial and post-test measurements should be recorded.

Statistical analysis: The statistical basis for reliability claims, including sample size justification, confidence level calculations, and any assumptions, must be transparent. Reviewers should be able to verify the analysis.

Conclusions and limitations: Clear statements of demonstrated reliability, along with any limitations or caveats, enable appropriate use of the demonstration results. Conditions where the demonstration may not apply should be identified.