Simulation-driven design has become indispensable across industries from aerospace to consumer electronics for predicting how systems will behave before committing to physical prototypes. However, the axiom "garbage in, garbage out" remains eternally relevant - if input data and models lack fidelity, the output results lose meaning.
This article provides practical guidance on properly setting up and validating simulations of printed circuit boards (PCBs) within electronic products. Principles for quality results apply broadly across modeling electromagnetic performance, thermal dissipation, mechanical stresses, manufacturing sensitivities, and more. Capturing correct design data along with realistic use conditions and margins is emphasized as the necessary foundation. Common missteps are highlighted to steer engineers towards maximizing the utility of simulation-based analysis and decision making.
The Risks of GIGO
Before outlining proactive practices, the risks of "garbage in, garbage out" merit emphasis:
- False Confidence - Elegant looking simulation visuals provide an illusion of validated performance without genuine predictive value. This compounds by propagating through additional engineering decisions.
- Over-Designing - Attempting to accommodate inaccurately severe simulated conditions adds unnecessary cost, size, weight and other drawbacks.
- Under-Designing - Simulations indicating deceptively mild conditions lead to unpleasant surprises late in development as issues emerge. Re-spins grow exponentially expensive.
- Misguided Decisions - Inaccurate modeling misrepresents tradeoffs, leading engineering in fruitless directions wasting immense time and money.
- Damaged Reputation - Consistently wrong predictions erode confidence in analysis credibility within an organization and with customers.
While simulations will always balance precision versus practicality, following guidelines to maximize model fidelity protects against these substantial pitfalls.
Capturing Design Intentions
The first line of defense is accurately capturing design embodiment details within the virtual modeling environment:
PCB Layout
Having the latest board layout is a clear prerequisite, but additionally:
- Verify layer stacks, laminate materials, finishes, etc. match current rev as built.
- Incorporate layout tweaks back into the master ODB++ file.
- Model any embedded components like capacitors or resistors.
- Correctly reference layout data across disciplinary simulation tools.
Components
- Capture exact parts with unique part numbers planned for production, not generics.
- Import 3D models, manufacturer SPICE models, mechanical specs, etc.
- Designate orientation and pin patterns.
- Include any added auxiliary parts.
Enclosure
- CAD imports of chosen external housing design.
- Properly locate PCB within housing and add connectors.
- Account for any thermal management parts like heat sinks.
Assembly
- Model intended structural supports, fasteners, and stiffeners.
- Capture PCB interfacing with other components.
- Include production process conditions like press fits.
Meticulously constructing each simulation model to closely match the envisioned as-built product provides the foundation for meaningful analyses, even before applying operating loads and environments. Garbage in begins with discrepancies here.
Defining Realistic Operating Conditions
The second critical aspect is applying stimulus representing realistic deployed conditions across parameters:
Electrical Loads
- Enter actual voltage and current levels intended rather than defaults.
- Waveforms should capture harmonics and transients expected.
- Model ranges from power off to surge overvoltage.
- Consider both typical and extreme use cases.
Thermal Loads
- Locate heat sources representing operating power electronics.
- Forced air convection with measured flow rates and fan curves.
- Apply external temperature profiles across service environments.
- Account for different modes like sleep versus active cycling.
Mechanical Loads
- Mounting constraints should match structural attachments.
- Vibration spectra per application environments.
- Simulate handling loads, drop events, etc.
- Any applied pressures matching enclosure sealing.
Environmental Conditions
- Temperature, pressure, humidity profiles by location.
- Corrosive atmospheres based on use, e.g. salt spray.
- Explosive risk zoning for dangerous deployments.
Mission Duration
- Simulate aging by applying loads over total lifetime target - often multiple years.
- Consider duty cycles representing uptime percentages.
Correctly configuring simulations based on measured prototyped performance and intended operating contexts, instead of purely theoretical approximations, achieves “garbage out” minimized analyses.
Adding Margins to Accommodate Variability
Simply capturing nominal design targets and environments remains insufficient when dealing with the realities of manufacturing variabilities and partial data. Some guardbanding principles include:
Parametric Tolerance Analysis
Run monte carlo sensitivity studies with parameters like trace widths, dielectric constants, ball grid array coplanarity, etc. sampled from statistical distributions based on achieved production capability. This reveals margin shortfalls.
Margin Slack Values
Manual blanket derating or oversizing of constraints helps accommodate uncertainties. Apply 10-20% pads for maximum temperatures, voltage ripple, torque loads, etc. against specs.
Conservative Assumptions
Where data is partially unavailable, make reasonably unfavorable assumptions that emphasize weaknesses rather than mitigations, ensuring identified issues are addressed.
Emphasize Corners
Focus simulations on worst case combinations of conditions across operating modes, physical locations in assembly, parts of duty cycle waveforms, and environmental temperatures and vibration spectra.
Monitor Correlation Trends
As more measured data including experimental test results becomes available from prototypes, track whether predictions err conservatively or liberally across simulations to date. If bias emerges, increase or decrease global margins.
Incorporating thoughtful guardbands protects against interpreting precision simulations as higher confidence than the approximate real world where variability dominates.
Validating Against Physical Testing
The strongest defense against piping garbage from simulations into decision making is rigorous validation through measured data from physical test cases:
Simple Tabletop Tests
Spot check limited simulations using simple benchtop experiments, before investing in complex qualification testing. Example principles:
- Measure power supply loading for digital logic vs. predicted power integrity simulations.
- Check thermocouple temperatures vs. thermal simulation hot spots.
- Verify connector strain relief compliance with hand torque wrenches.
Engineering Qualification
Formalize comparison metrics between simulation outputs and formal qualification test procedure measurements:
- Tabulate pass/fail performance against environmental stress screening profiles.
- Log overtesting ratios for how much margin was present.
- Characterize degradations as a percentage of total specification.
Accelerated Life Testing
Run intensive experiments pushing products far beyond normal use conditions to uncover hidden sensitivities:
- Rapid thermal cycling across extended minimum/maximum ranges
- Highly amplified vibration loads approaching destructive limits
- Pull testing failure limits on structural attachments
While avoiding over-reliance on simulations, proactively correlating modeled predictions against measured data from physical testing inoculates against persisting garbage outputs infecting engineering decisions.
Institutionalizing Best Practices
Achieving reliable, high fidelity simulations with minimal garbage risks requires not just diligent individual engineers but organization-wide commitments to best practices via:
Standard Operating Procedures
- Documentation for simulation setup, validation, and handoff.
- Template projects with predefined parameters and modeling scope.
- Centralized database sources as single version of truth.
Cross-Disciplinary Alignment
- Unified modeling conventions, parameters and assumptions across teams.
- Regular technical review of practices.
- Liaisons across departments.
MBSE Model Management
-automated model version control.
- Configuration linkages across analyses.
- Regression testing frameworks.
Simulation Governance
- Data QA, validation requirements.
- Accuracy benchmarking.
- Training programs.
Instituting these foundational policies, resources and cultures enterprise-wide reduces inconsistent and unvalidated practices enabling garbage simulation outputs to persist.
Conclusion
While convoluted modeling complexity receives much praise, thoughtfully realistic simulation inputs and context followed by physical prototype validation is indispensable to genuinely inform engineering decisions with confidence. The core principles for avoiding "garbage in, garbage out” pitfalls include:
- Meticulously constructing as-designed models
- Judiciously applying operating conditions
- Prudently derating constraints
- Continuously correlating physical test data
- Committing organizationally to rigor
Applying these guidelines within a systems engineering framework will uphold simulations as a keystone capability for accelerating development across industries while circumventing the hazards of speculative or inaccurate predictions. Disciplined processes prevent garbage in, and therefore garbage out.
Frequently Asked Questions
Q: If simulation accuracy will always be approximate, why spend so much effort on marginal improvements?
A: While perfect validation is impossible, even incremental improvements to fidelity and correlations avoid disastrous divergence between models and reality. Given simulation's exponential leverage over downstream decisions, accuracy merits extreme diligence.
Q: How can simulating corner cases provide realistic confidence if those extremes never actually simultaneously occur?
A: Stress testing reveals sensitivities
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