Custom RF EDA apps.
Made to order.
Your measured data, simulations, and benches — wired into the tools your RF team actually uses.
30 min • Bring a specific RF workflow bottleneck • No discovery call — bring data, not a deck
Custom RF EDA apps.
Made to order.
Your measured data, simulations, and benches — wired into the tools your RF team actually uses.
30 min • Bring a specific RF workflow bottleneck • No discovery call — bring data, not a deck
Recognize These?
The RF workflow gaps your EDA stack was never built to close.
"Your HFSS sim and the bench measurement disagree by 2 dB. You're rebuilding the prototype."
The calibration drifted somewhere between schematic, layout, fab, and assembly — and nothing in your stack closes the loop between sim and bench. So you discover the gap with a prototype, a network analyzer, and a soldering iron.
"Your senior RF engineer's intuition isn't in any EDA tool you license."
"If IP3 is within 2 dB of spec, derate the bias current 10% before tape-out." That judgment lives in one head, not in ADS, not in HFSS. When they leave, your team starts from the datasheet.
"You licensed ADS, HFSS, and AWR — and your team still stitches results in Excel."
Each EDA tool is great inside its own domain. None of them talk to each other natively. Engineers burn hours moving S-parameters between formats, validating manually, and chasing version mismatches between tool, model, and bench.
None of these are RF problems — which is why throwing your best engineer at them hasn't worked.
Recognize These?
The RF workflow gaps your EDA stack was never built to close.
"Your HFSS sim and the bench measurement disagree by 2 dB. You're rebuilding the prototype."
The calibration drifted somewhere between schematic, layout, fab, and assembly — and nothing in your stack closes the loop between sim and bench. So you discover the gap with a prototype, a network analyzer, and a soldering iron.
"Your senior RF engineer's intuition isn't in any EDA tool you license."
"If IP3 is within 2 dB of spec, derate the bias current 10% before tape-out." That judgment lives in one head, not in ADS, not in HFSS. When they leave, your team starts from the datasheet.
"You licensed ADS, HFSS, and AWR — and your team still stitches results in Excel."
Each EDA tool is great inside its own domain. None of them talk to each other natively. Engineers burn hours moving S-parameters between formats, validating manually, and chasing version mismatches between tool, model, and bench.
None of these are RF problems — which is why throwing your best engineer at them hasn't worked.
Custom EDA Apps We've Built.
Results We've Measured.
What works is software written for the seam itself — small, specific apps that plumb the gap your stack leaves open, instead of trying to be another stack. Antenna pattern reconciliation engines that fuse HFSS sims with chamber measurements. Phased-array calibration coefficient extractors with mutual-coupling correction. Beam codebook generators with sidelobe and null-steering constraints. ML surrogates that turn week-long HFSS sweeps into sub-second lookups. Sim-to-bench calibration loops that close themselves.
Whatever the gap is in your workflow — written to your specification, deployed in your environment, and yours to keep. Below: four deployed apps with before-and-after data, and one you can try right now.
Built with whatever earns its keep: physics-informed models, Monte Carlo, surrogate ML, Bayesian DOE, RAG over engineering notes — picked per problem, not per fashion.
Parametric Yield Predictor
Monte Carlo tolerance app that connects RF performance specs (gain flatness, return loss, IP3, group delay) to PCB process limits. Predicts yield before tape-out — replacing the prototype-and-pray cycle with data-driven go/no-go decisions at design time.
Topology Rule Engine
Automated RF DfM validation that runs at design time, not at review. Encodes senior-engineer rules — trace impedance vs. feature size, via stitching, ground return paths, controlled-impedance constraints — into executable checks that fire on every layout iteration.
Automated Data Bridge
Cross-tool pipeline that moves HFSS / ADS / AWR outputs into downstream test, measurement, and ERP systems. Eliminates manual re-entry, Touchstone format conversion, and the silent data corruption at every handoff between simulator, lab bench, and yield database.
Touchstone Viewer
Drag-and-drop S-parameter visualization in the browser. Spot-check a .s2p / .s4p in 5 seconds without firing up the full toolchain. A working sample of how we build niche RF apps — try it before you book.
Custom EDA Apps We've Built.
Results We've Measured.
What works is software written for the seam itself — small, specific apps that plumb the gap your stack leaves open, instead of trying to be another stack. Antenna pattern reconciliation engines that fuse HFSS sims with chamber measurements. Phased-array calibration coefficient extractors with mutual-coupling correction. Beam codebook generators with sidelobe and null-steering constraints. ML surrogates that turn week-long HFSS sweeps into sub-second lookups. Sim-to-bench calibration loops that close themselves.
Whatever the gap is in your workflow — written to your specification, deployed in your environment, and yours to keep. Below: four deployed apps with before-and-after data, and one you can try right now.
Built with whatever earns its keep: physics-informed models, Monte Carlo, surrogate ML, Bayesian DOE, RAG over engineering notes — picked per problem, not per fashion.
Parametric Yield Predictor
Monte Carlo tolerance app that connects RF performance specs (gain flatness, return loss, IP3, group delay) to PCB process limits. Predicts yield before tape-out — replacing the prototype-and-pray cycle with data-driven go/no-go decisions at design time.
Topology Rule Engine
Automated RF DfM validation that runs at design time, not at review. Encodes senior-engineer rules — trace impedance vs. feature size, via stitching, ground return paths, controlled-impedance constraints — into executable checks that fire on every layout iteration.
Automated Data Bridge
Cross-tool pipeline that moves HFSS / ADS / AWR outputs into downstream test, measurement, and ERP systems. Eliminates manual re-entry, Touchstone format conversion, and the silent data corruption at every handoff between simulator, lab bench, and yield database.
Touchstone Viewer
Drag-and-drop S-parameter visualization in the browser. Spot-check a .s2p / .s4p in 5 seconds without firing up the full toolchain. A working sample of how we build niche RF apps — try it before you book.
Generic EDA Tools Require Mastery.
Custom EDA Apps Embed It.
Your stack — ADS, HFSS, AWR, MATLAB — amplifies whoever uses it. The senior engineer gets a 10× answer; the new hire gets noise. We build the niche apps that ship the question already framed.
Runs the math you ask for.
- Tool-driven, not problem-driven. The user has to know which sweep to set up, which corner to simulate, which assumption is hiding the answer, which post-processing script to write.
- Amplifies the master, strands the novice. The same HFSS solve, ADS harmonic balance, AWR parameter sweep, or MATLAB RF Toolbox script produces a great answer for a senior engineer and noise for everyone else.
- Reasoning lives in heads. When the senior RF engineer leaves, takes a sabbatical, or moves teams — the capability leaves with them.
Encodes the right question.
- RF physics, wired in. Trade-offs across RF performance, manufacturing tolerance, signal integrity, EMC, thermal, and mixed-signal interaction aren't something the user has to remember. They're encoded in the app, the same way your senior engineer encodes them in their head.
- Boundary-aware and bench-calibrated. Physics-informed against measured S-parameters and lab data, not vendor datasheets. The answer is the same whether your senior or your new grad is at the keyboard.
- Reasoning lives in the app. Your team inherits the senior-engineer logic on day one — and keeps it after they're gone.
- Statistical and ML methods where they earn it. Surrogate models that replace week-long HFSS sweeps with millisecond approximations. Bayesian optimization when the design space is too big to grid-search. Anomaly detection on measured S-parameters at production scale. We don't add ML for the brochure — we add it where the data supports it and the alternative is brute force.
You don't need another EDA tool. You need an app that already knows what your senior RF engineer knows.
Generic EDA Tools Require Mastery.
Custom EDA Apps Embed It.
Your stack — ADS, HFSS, AWR, MATLAB — amplifies whoever uses it. The senior engineer gets a 10× answer; the new hire gets noise. We build the niche apps that ship the question already framed.
Runs the math you ask for.
- Tool-driven, not problem-driven. The user has to know which sweep to set up, which corner to simulate, which assumption is hiding the answer, which post-processing script to write.
- Amplifies the master, strands the novice. The same HFSS solve, ADS harmonic balance, AWR parameter sweep, or MATLAB RF Toolbox script produces a great answer for a senior engineer and noise for everyone else.
- Reasoning lives in heads. When the senior RF engineer leaves, takes a sabbatical, or moves teams — the capability leaves with them.
Encodes the right question.
- RF physics, wired in. Trade-offs across RF performance, manufacturing tolerance, signal integrity, EMC, thermal, and mixed-signal interaction aren't something the user has to remember. They're encoded in the app, the same way your senior engineer encodes them in their head.
- Boundary-aware and bench-calibrated. Physics-informed against measured S-parameters and lab data, not vendor datasheets. The answer is the same whether your senior or your new grad is at the keyboard.
- Reasoning lives in the app. Your team inherits the senior-engineer logic on day one — and keeps it after they're gone.
- Statistical and ML methods where they earn it. Surrogate models that replace week-long HFSS sweeps with millisecond approximations. Bayesian optimization when the design space is too big to grid-search. Anomaly detection on measured S-parameters at production scale. We don't add ML for the brochure — we add it where the data supports it and the alternative is brute force.
You don't need another EDA tool. You need an app that already knows what your senior RF engineer knows.
Pick the EDA Engagement
That Fits Your Problem
Every engagement includes methodology transfer — we don't create dependency, we build capability. The app and its documentation are yours.
RF Workflow Audit
Map your RF design-to-test workflow. Identify where your EDA stack — ADS, HFSS, AWR, MATLAB — leaves gaps. Get a custom EDA app architecture spec before committing to a build.
- RF workflow map (sim → layout → fab → test)
- EDA stack gap analysis
- Bottleneck ranking by time + cost
- Custom EDA app architecture spec
- 2–4 week engagement
Custom EDA App
Build and deploy the niche EDA app your stack doesn't ship — Monte Carlo yield predictor, RF DfM rule engine, HFSS post-processor, S-parameter pipeline, or whatever the audit surfaced. Physics-informed, deployed alongside your toolchain.
- Physics-informed RF EDA app
- Integrated with ADS / HFSS / AWR / MATLAB
- Team onboarding + documentation
- Before-and-after measurement
- 6–14 week engagement
Living EDA System
Custom EDA app + live bench data feeds + continuous recalibration against measured S-parameters and lab results. The intelligence layer that survives contact with real silicon.
- App connected to live bench data
- Continuous model recalibration
- Monte Carlo + measurement validation
- Decision logic + ERP integration
- 14–24 week deployment
EDA App Retainer
Custom EDA apps evolve as your process does. Monthly retainer to recalibrate models against new bench data, iterate app features, identify the next RF workflow gap, and keep the decision logic current.
- Monthly improvement metrics
- Model recalibration + app feature updates
- New RF workflow gap identification
- Month-to-month, no lock-in
Pick the EDA Engagement
That Fits Your Problem
Every engagement includes methodology transfer — we don't create dependency, we build capability. The app and its documentation are yours.
RF Workflow Audit
Map your RF design-to-test workflow. Identify where your EDA stack — ADS, HFSS, AWR, MATLAB — leaves gaps. Get a custom EDA app architecture spec before committing to a build.
- RF workflow map (sim → layout → fab → test)
- EDA stack gap analysis
- Bottleneck ranking by time + cost
- Custom EDA app architecture spec
- 2–4 week engagement
Custom EDA App
Build and deploy the niche EDA app your stack doesn't ship — Monte Carlo yield predictor, RF DfM rule engine, HFSS post-processor, S-parameter pipeline, or whatever the audit surfaced. Physics-informed, deployed alongside your toolchain.
- Physics-informed RF EDA app
- Integrated with ADS / HFSS / AWR / MATLAB
- Team onboarding + documentation
- Before-and-after measurement
- 6–14 week engagement
Living EDA System
Custom EDA app + live bench data feeds + continuous recalibration against measured S-parameters and lab results. The intelligence layer that survives contact with real silicon.
- App connected to live bench data
- Continuous model recalibration
- Monte Carlo + measurement validation
- Decision logic + ERP integration
- 14–24 week deployment
EDA App Retainer
Custom EDA apps evolve as your process does. Monthly retainer to recalibrate models against new bench data, iterate app features, identify the next RF workflow gap, and keep the decision logic current.
- Monthly improvement metrics
- Model recalibration + app feature updates
- New RF workflow gap identification
- Month-to-month, no lock-in
The RAPID Framework
Every custom EDA app engagement follows the same five phases.
A battle-tested methodology you keep — along with the app.
Recognize
Map your RF design-to-test workflow end-to-end. We identify the gaps your EDA stack leaves — the places engineers stitch results manually, override defaults, or rebuild prototypes because the sim missed the bench.
Analyze
Quantify the gap in dollars, prototype cycles, and engineer-hours. We translate "the simulation didn't catch it" into capital, schedule, and headcount — and rank the candidates by ROI on a custom app.
Plan
Architect the custom EDA app. We define the physics models, the statistical and ML methods (surrogates, Bayesian DOE, anomaly detection — when they earn it), the data integrations (HFSS, ADS, AWR, MATLAB, bench instruments), the UX for the target engineer, and the numerical success criteria.
Implement
Precision deployment alongside your existing stack. We build the app, wire it into your toolchain, encode senior-engineer logic, and onboard your team.
Drive
Continuous optimization against bench data. We measure app performance against the baseline and iterate. You retain ownership of the app, the models, and the metrics.
The RAPID Framework
Every custom EDA app engagement follows the same five phases.
A battle-tested methodology you keep — along with the app.
Recognize
Map your RF design-to-test workflow end-to-end. We identify the gaps your EDA stack leaves — the places engineers stitch results manually, override defaults, or rebuild prototypes because the sim missed the bench.
Analyze
Quantify the gap in dollars, prototype cycles, and engineer-hours. We translate "the simulation didn't catch it" into capital, schedule, and headcount — and rank the candidates by ROI on a custom app.
Plan
Architect the custom EDA app. We define the physics models, the statistical and ML methods (surrogates, Bayesian DOE, anomaly detection — when they earn it), the data integrations (HFSS, ADS, AWR, MATLAB, bench instruments), the UX for the target engineer, and the numerical success criteria.
Implement
Precision deployment alongside your existing stack. We build the app, wire it into your toolchain, encode senior-engineer logic, and onboard your team.
Drive
Continuous optimization against bench data. We measure app performance against the baseline and iterate. You retain ownership of the app, the models, and the metrics.
The hardest RF problems don't live inside your EDA tools. They live between them.
We find the gaps, build the apps, and prove the fix.
Every RF engineer knows the moment. The HFSS sim says yes. The network analyzer says no. Six weeks of schedule disappear into a rebuild — and not because anyone made a mistake. Because nothing in the stack closed the loop between sim and bench for that specific problem.
Sparkreate Ops is a custom EDA app shop. We build the niche RF workflow tools the big EDA vendors don't ship — and don't have the customer count to justify shipping. Monte Carlo yield predictors, RF DfM rule engines, HFSS post-processors, S-parameter pipelines, sim-to-bench calibration loops. Deployed alongside your ADS, HFSS, AWR, or MATLAB stack. Not handed off in a PDF.
Born from a decade at the RF hardware-software boundary — RF frontend modules, mixed-signal integration, wireless production. Built by engineers who've shipped boards and written the EDA tools that ship them.
The hardest RF problems don't live inside your EDA tools. They live between them.
We find the gaps, build the apps, and prove the fix.
Every RF engineer knows the moment. The HFSS sim says yes. The network analyzer says no. Six weeks of schedule disappear into a rebuild — and not because anyone made a mistake. Because nothing in the stack closed the loop between sim and bench for that specific problem.
Sparkreate Ops is a custom EDA app shop. We build the niche RF workflow tools the big EDA vendors don't ship — and don't have the customer count to justify shipping. Monte Carlo yield predictors, RF DfM rule engines, HFSS post-processors, S-parameter pipelines, sim-to-bench calibration loops. Deployed alongside your ADS, HFSS, AWR, or MATLAB stack. Not handed off in a PDF.
Born from a decade at the RF hardware-software boundary — RF frontend modules, mixed-signal integration, wireless production. Built by engineers who've shipped boards and written the EDA tools that ship them.
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