Lower Hallucination Pendedahan
Kritikal engineering results are parsed, validated and linked to evidence outside the AI model.
Silicon Engineering Arahan Pusat
Repeatable IC Design.
Traceable Decisions.
Lebih Pantas Verification.
POWERED BYAINNA NeuralOps
A governed semiconductor intelligence architecture that routes each engineering task to the safest, most repeatable and most compute-efficient processing layer.
Deterministic where possible. AI only where necessary. Manusia-approved where critical.
Engineering-assisted, human-controlled. Final approval remains with qualified engineers.
RequirementSchematicRTLDRC reportLVS reportTiming reportPVT resultLiputan report
CONTROL LAYERAINNA NeuralOps
OrchestrationSanitise · Segment · Klasifikasi · Route
OrchestrationSanitise · Segment · Klasifikasi · Route
Rules + Parsers BYPASS AITempatan Small Model LIGHTWEIGHTAdvanced Model ONLY IF REQUIRED
Independent Validation→Evidence Pack→Engineer Sign-Off
WaferDieFloorplanNeuralOpsManusia Authority
Higher Engineering Repeatability
Design revisions, PDK versions, tool environments, scripts, seeds and evidence remain traceable.
Lower Compute Demand
Structured tasks use parsers, rules, cached results and detached services instead of unnecessary large-model execution.
Stronger Kedaulatan Data
Confidential PDK, IP and semiconductor project data can remain within customer-controlled infrastructure.
Sanitise
Protect proprietary IC data, PDK information, credentials and engineering context.
Segment
Separate requirements, DRC, LVS, timing, coverage, PVT, Monte Carlo and waivers.
Parse
Use artefact-specific engineering parsers instead of one general-purpose model.
Validate
Check values, units, versions, completeness and specification limits deterministically.
Route
Select rules, databases, local models or advanced reasoning by risk and complexity.
Assist
Use AI for interpretation, comparison, synthesis and engineering explanation only.
Verify
Bandingkan AI-assisted findings against independent evidence and validators.
Manusia Sign-Off
Keep architecture, waiver, verification and tape-out authority with engineers.
COMPUTE-EFFICIENT BY ARCHITECTUREFewer unnecessary model calls. Lower compute demand. Lagi accountable semiconductor engineering.
Parser-First MemprosesSmall-Model-First PenghalaanGPU Only When RequiredCached Validated KeputusanTempatan Memproses SupportedMeasurable Compute Telemetry
AINNA is the engineering intelligence, orchestration, traceability and repeatability layer around existing semiconductor design alatan.
AINNA does not replace Cadence, Synopsys, Siemens EDA, Ansys, COMSOL or other semiconductor engineering alatan. It connects requirements, workflows, results, decisions and engineering evidence across the IC development lifecycle.
New · Semicon
Your inputs6
Silicon-ready artifacts
6 input → artifak sedia silikon
Rendah-Compute Semiconductor Intelligence
NeuralOps for Efficient and Governed Silicon Engineering
Semiconductor engineering cannot rely on a single probabilistic model. NeuralOps separates deterministic processing, specialised parsing, AI reasoning and human authority into controlled layers.
CONTROLLED PROCESSING ARCHITECTUREAdvanced AI is one routed layer, not the system
01SEMICONDUCTOR ENGINEERING INPUT
↓02SECURE PROJECT BOUNDARY
↓03SANITISATION + DATA CLASSIFICATION
↓04TASK SEGMENTATION
↓05SPECIALISED ENGINEERING PARSERS
↓06DETERMINISTIC VALIDATION
↓07SMART ROUTING
↓ Rules and Formula Engines
Timing limits · unit checks · PVT completeness
Approved Databases and Cached Keputusan
Requirements · validated baselines · prior evidence
Detached Engineering Perkhidmatan
DRC/LVS parsing · manifests · checksums · statistics
Tempatan Small Model
Lightweight classification · metadata · document tagging
Advanced Penaakulan Model
Kompleks comparison · ambiguity · synthesis · explanation
INDEPENDENT OUTPUT VALIDATION↓EVIDENCE PACK↓QUALIFIED ENGINEER SIGN-OFF
Semiconductor-Specific Control Map
One Seni Bina. Six Engineering Domains.
Select a domain to see the bounded NeuralOps controls used around the engineering workflow.
Requirements Engineering
Requirement extraction
Requirement-ID validation
Ambiguity detection
Design-block mapping
Test mapping
Perubahan-impact analysis
Missing-evidence detection
Analog and Mixed-Signal Design
Simulasi manifest
PVT parsing
Monte Carlo aggregation
Unit validation
Specification-margin checks
Post-layout comparison
Evidence linking
Digital and RTL Design
Lint-report parsing
CDC and RDC parsing
Synthesis-result ingestion
Timing extraction
Regresi tracking
Liputan aggregation
ECO traceability
Physical Design
Floorplan revision tracking
Congestion-result parsing
DRC parsing
LVS parsing
PEX result linking
IR-drop status
Electromigration status
Sign-off evidence completeness
Verification
Test-plan mapping
Regresi segmentation
Failure clustering
Liputan closure
Waiver validation
Unreproduced failure detection
Evidence-bound summaries
Produk, Test and Yield
Test-data ingestion
Wafer-map generation
Bin-yield analysis
Tester correlation
Parametric distribution
Simulasi-to-silicon comparison
Failure-analysis traceability
ESG-Aware Silicon Operasi
Lower-Compute AI for Semiconductor Operasi
Simulasi logs, regressions, verification data, DRC/LVS output, timing reports, wafer-test records, yield data, characterisation results and engineering documents are often structured. They should not automatically be sent to a large AI model.
NeuralOps is designed to reduce unnecessary model execution through smart routing, detached processing and specialised parsers.
- FirstRules, formulas and deterministic validation
- ThenSpecialised parsers and approved databases
- ThenCached and previously validated results
- ThenSmall local models for lightweight classification
- FinallyAdvanced reasoning models only for complex analysis
Fewer unnecessary GPU callsReduced token processingReduced repeated analysisLower data-transfer requirementsHigher cache reuseLagi local processingLower infrastructure demandLagi predictable compute usageReduced operational wasteImproved auditability
NeuralOps is designed to reduce unnecessary compute consumption by routing work to the lightest validated processing layer capable of completing the task.
Every report → Large model → Large context → Repeated generation → Manual checking
- Higher compute demand
- Greater hallucination surface
- Weak reproducibility
- Repeated token processing
- Lagi external data movement
- Higher validation burden
Report → Sanitise → Segment → Parse → Validate → Route → AI only where needed → Evidence → Manusia sign-off
- Designed for lower unnecessary AI usage
- Supports predictable processing
- Stronger evidence traceability
- Better confidentiality controls
- Greater output control
- Designed for lower compute demand
Carbon Claim Readiness
Evidence Level Controls the Bahasa
An estimated result can never be automatically presented as measured or independently verified.
Seni Bina Statement
NeuralOps is designed to reduce unnecessary model execution through smart routing, detached processing and specialised parsers.
Published architecture statementSumber: AINNA NeuralOps architecture specification · Last reviewed: 2026-07-29Estimated Projection
Estimated reduction based on configured workload, hardware, routing and grid assumptions.
Estimated Projection - Not a Certified Carbon AuditSumber: AINNA Jejak Karbon Emulator configuration · Last reviewed: 2026-07-29Open Jejak Karbon EmulatorMeasured Operational Result
Publication blocked until this evidence level passes its governance and approval requirements.
Not available for publicationSumber: Production telemetry - not yet connected · Last reviewed: 2026-07-29Verified Independent Claim
Publication blocked until this evidence level passes its governance and approval requirements.
Not available for publicationSumber: Independent assurance - not yet available · Last reviewed: 2026-07-29Operational Evidence Schema
Semiconductor Compute Telemetry
Operational metadata is stored separately from proprietary design artefacts. ESG measurement does not require collecting schematics, RTL or confidential report content.
event_idproject_idcustomer_environmenttimestamp_starttimestamp_endtask_typeengineering_domainrisk_levelprocessing_routeparser_nameparser_versionmodel_namemodel_sizemodel_locationinput_tokensoutput_tokenscached_tokensgpu_runtime_secondscpu_runtime_secondsmemory_usageestimated_energy_kwhmeasured_energy_kwhenergy_sourcemeter_idmeasurement_sourceevidence_checksumdata_transfer_mbcache_hitdetached_system_usedadvanced_model_usedvalidation_statusresult_statushardware_typehardware_regiondata_centre_puegrid_emission_factorcalculation_methodmethodology_versionexcluded_workloadsconfidence_rangeuncertainty_rangeFUTURE-SIAP COMPONENT
NeuralOps Compute and Carbon Evidence Pack
Demonstration DataEstimatedOperationally MeasuredInternally ReviewedIndependently Verified
Measurement periodDefined workloadAI-heavy baselineNeuralOps routing distributionDetached-system workloadParser workloadTempatan-model workloadAdvanced-model workloadModel calls avoidedCache hit rateGPU runtimeCPU runtimeEstimated or measured energyTenaga source or meter identityEvidence checksumPUE assumptionGrid-emission factorEstimated carbon footprintGaris Dasar carbon footprintReduction estimateCalculation methodologyExcluded workloadsKeyakinan and uncertainty rangeAssumptionsLimitationsData completenessKelulusan recordVerification status
Semiconductor ESG and Compute Papan Pemuka
Route-Level Compute Evidence
Model the Seni Bina
Bandingkan AI-Heavy and Smart-Routed Memproses
Configure workload, hardware, routing, PUE and grid assumptions before making an unjuran anggaran.
Carbon metrics disclaimer: Carbon and energy figures shown on this website may be estimates based on configured workload, hardware, PUE and grid-emission assumptions. They are not certified carbon-audit results unless explicitly identified as independently verified. Ulasan assumptions in the Jejak Karbon Emulator.
Engineering Friction
Semiconductor Engineering Problems We Solve
Operational problems that slow design closure, weaken evidence and make knowledge difficult to reuse.
Fragmented Design Knowledge
Specifications, scripts, simulation settings and rationale are scattered.
- Operational impact
- Slow onboarding, duplicated investigation and key-person dependency.
- AINNA control
- Links decisions, artefacts and lessons to controlled design context.
Non-Repeatable Engineering Runs
PDKs, alatan, models, scripts, seeds and environments drift.
- Operational impact
- Keputusan become difficult to reproduce or compare.
- AINNA control
- Captures a complete, checksum-aware Run Manifest.
Weak Requirements Kebolehkesanan
Requirements are not consistently connected to blocks, tests and evidence.
- Operational impact
- Perubahan impact and audit readiness remain uncertain.
- AINNA control
- Maintains requirement-to-evidence traces and missing-test detection.
Slow Verification Closure
Failures, coverage gaps, waivers and defects require manual correlation.
- Operational impact
- Regresi triage consumes senior engineering time.
- AINNA control
- Clusters failure signatures and exposes closure blockers.
Terhad Pengurusan Visibility
Status reports are manually assembled and quickly become stale.
- Operational impact
- Gate decisions rely on incomplete programme context.
- AINNA control
- Builds role-aware, evidence-backed readiness dashboards.
Lost Learning Between Projek
Resolved issues and design rationale are not retained as reusable knowledge.
- Operational impact
- Known failures return and reuse risk increases.
- AINNA control
- Preserves approved patterns, limitations and findings in a Knowledge Vault.
IC Development Lifecycle
From Requirements to Silicon Evidence
Follow one controlled thread from a specification through design, simulation, verification and human sign-off.
Demonstration Data
REQUIREMENTREQ-ANA-0042
Input-Referred Noise Density ≤ 12 nV/√Hz
Operating Temperature: -40°C to 125°C
Verification Requiredmaps to
DESIGN BLOCKAnalog Front EndOwner: ANA-02 · Rev C7
Sensor→
Analog Front End→
ADC→
Digital Memproses→
Interface
Signal intent and interface assumptions remain linked to the requirement baseline.
ANALOG CONTEXT
Rendah-Noise AFE
- Schematic: afe_top_C7
- Device parameters: verified
- PDK reference: validated input required
- Simulasi setup: noise_corner_v12
DIGITAL CONTEXT
Control RTL
- Hierarchy: sensor_ctrl
- Module version: 2.8.1
- Constraints: ctrl_sdc_19
- Git commit: 8c2f1e7
- Owner: RTL-04
always_ff @(posedge clk)
if (!rst_n) state <= IDLE;
if (!rst_n) state <= IDLE;
VoltageNoiseClockTemperature response
TT / 0.90 V / 25°C PASSSS / 0.81 V / 125°C PASSFF / 0.99 V / -40°C WARNINGSF / 0.81 V / 85°C FAIL
Functional coverage91%
Code coverage94%
Assertion coverage88%
Requirements with linked tests86%
Regresi pass rate93%
Open defects14
Waivers7
Requirement coverage means requirements with at least one linked verification test and evidence.
CPU
SRAM
AFE
NoC
I/O
Placement LengkapClock tree CleanCongestion 2 hotspotsIR drop UlasanDRC 2 markersTiming WNS +0.03ns
87%Tape-Out Readiness
RequirementslinkedDesign revisionlinkedSimulasi resultslinkedRun manifestslinkedLiputanlinkedWaiverslinkedFMEAlinkedSign-off checklistlinkedManusia approvalslinked
Controlled Engineering System
The AINNA IC DesignOps Platform
Modul connect engineering context without replacing qualified engineers or licensed EDA systems.
MODULE 01
Requirements Intelligence
- Requirement IDs and revisions
- Ambiguity detection
- Block, test and evidence mapping
- Perubahan-impact analysis
- Missing-test detection
- Aliran kerja kelulusan
MODULE 02
Engineering Knowledge Vault
- Approved design patterns
- Seni Bina decisions
- Known IP limitations
- Failure signatures
- Silicon anomalies
- Verification templates
- Foundry clarification
MODULE 03
Design Decision Records
- Alternatives and rationale
- Supporting evidence
- PPA and cost impact
- Risiko and approver
- Invalidating conditions
MODULE 04
Golden Flow Registry
- Analog simulation
- RTL verification and synthesis
- Timing and physical design
- DRC, LVS and PEX
- Kebolehpercayaan and MEMS
- Tape-out and characterisation
MODULE 05
Verification Intelligence
- Verification-plan tracking
- Regresi monitoring
- Failure clustering
- Liputan closure
- Waiver and defect aging
- Readiness gates
MODULE 06
IP Reuse Intelligence
- Berkelayakan nodes
- Previous silicon usage
- Verification status
- Known issues and dependencies
- Licensing constraints
- Reuse readiness and owner
MODULE 07
Design Evidence Pack
- Requirements baseline
- Assumptions and constraints
- Run manifests and reports
- Liputan, waivers and FMEA
- CTQ and known limitations
- Manusia approval and audit trail
Detached · Segmented · Evidence-Bound
AI Must Never Become a Single Point of Engineering Failure
Semiconductor engineering requires repeatability, traceability and evidence. AINNA NeuralOps separates deterministic engineering processes from probabilistic AI reasoning so that an AI-generated response cannot silently become a design, verification or tape-out decision.
Gunakan AI termaju hanya apabila kecerdasan termaju benar-benar diperlukan.
Keep validation-critical work deterministic
Most semiconductor workloads do not require a large language model. Structured and repetitive work belongs in controlled processing layers.
Deterministic rulesSpecialised parsersSchema validatorsMathematical checksVersi-control checksPangkalan Data queriesApproved workflow enginesDetached servicesSmall local models where appropriate
Activate advanced reasoning selectively
Model lanjutans are reserved for work where interpretation and synthesis add genuine engineering value.
Kompleks reasoningCross-document interpretationAmbiguity analysisEngineering comparisonRoot-cause hypothesesKnowledge synthesisManusia-readable explanation
This boundary is designed to reduce unnecessary model calls, token usage, compute demand, latency, cost, power consumption, hallucination exposure and uncontrolled data movement.
01Engineering Input
02Keselamatan Boundary
03Sanitisation
04Segmentation
05Data Pengelasan
06Specialised Parser
07Deterministic Validation
08Smart Routing
09Detached / Approved AI
10Independent Validator
11Evidence Pack
12Manusia Sign-Off
Stage 1 of 12: Engineering document enters the controlled boundary.
Detached and Deterministic Memproses96%
Advanced AI Required4%
The 96% demonstration share means advanced reasoning models are bypassed; it includes deterministic, cached and customer-controlled local processing and does not claim that every bypassed task is deterministic.
Independent Engineering Perkhidmatan
Sistem Berasingan
Sistem Berasingan are engineering services that operate independently from the language model. They execute controlled, repeatable and auditable tasks without depending on AI-generated reasoning.
Model AI tidak bertanggungjawab untuk mengesahkan outputnya sendiri.Engineering Input → Large AI Model → AI Interpretation → Dijana AI Result → Engineer Reviews Everything
- Unnecessary AI dependency
- High compute usage
- Difficult repeatability
- Increased hallucination surface
- Weak evidence separation
Input → Sanitisation → Segmentation → Parser → Validation → Detached Perkhidmatan → AI hanya apabila diperlukan → Kelulusan Manusia
- Lower AI exposure
- Higher repeatability
- Better traceability
- Lower compute demand
- Clear responsibility boundaries
File validationSchema validationPDK-version checksTool-version checksHash verificationRun-manifest generationRequirements ID validationTest-result ingestionLiputan calculationRegresi statusDRC / LVS parsingPVT matrix generationMonte Carlo aggregationStatistical calculationsCapability indicesWafer-map generationEvidence-pack assemblyPermission checksRetention enforcementAudit-log generationKelulusan-state management
Decompose Before Penaakulan
Segmentation Before Intelligence
A request such as “Ulasan this post-layout verification package and determine tape-out readiness” is never sent as one uncontrolled prompt. It becomes bounded tasks with defined inputs, output schemas, allowed alatan, risk, classification, validation, evidence and human gates.
- Validate files and formats
- Identify project and revision
- Confirm PDK and tool versions
- Parse DRC results
- Parse LVS results
- Parse timing results
- Parse coverage data
- Parse waiver records
- Bandingkan against approved limits
- Identify missing evidence
- Generate technical ringkasan
- Route unresolved issues to engineers
Deterministic
File validation, numeric calculation, checksum, lookup
AI not permitted or unnecessaryRendah-Risk Assisted
Pengelasan, metadata, formatting, basic ringkasan
Parser or small local model preferredEngineering Analysis
Ambiguity, failure clustering, cross-report comparison
Evidence-grounded model permittedHigh-Risk Decision Support
Waiver, sign-off, safety or reliability analysis
Pengesahan bebas dan kelulusan manusia mandatoryProhibited Autonomi Tindakan
Tape-out, PDK change, production release, destructive action
Autonomi execution prohibitedDefined inputNormalised timing metrics + REQ-TIM baseline
Hasil schemarequirement_id · value · unit · limit · status
Allowed alatanRule engine + approved-limit database
Risk / data classL0 deterministic · Confidential IC
Validation / evidenceUnit check + baseline checksum
Autonomy / approvalBandingkan only · engineer approves disposition
Untrusted by Default
Sanitise Before Memproses
Engineering data, user instructions, embedded text, external content and system instructions remain separate. Instructions inside uploaded files can never override security, validation or approval rules.
Uploaded File→File + MIME Validation→Malware / Archive Check→Content Extraction→Prom-Injection Detection→Confidentiality Pengelasan→Metadata Normalisation→Approved Memproses Zone
PASS Original and sanitised checksums, transformations and policy decision are written to the audit record.
BLOCK / QUARANTINE Invalid, malicious or policy-conflicting content cannot enter the processing zone.
PRIVATE ROUTE Sensitive content remains in the approved private environment, with identifiers minimised and access privileges enforced.
File-size and encoding limitsExecutable and script removalMacro and embedded-object detectionHidden-content inspectionLuaran-link validationSecret and credential detectionPDK and proprietary IP detectionPersonal-data detectionPelanggan-project isolationPeribadi-route policy decision and audit log
Artefact-Specific Extraction
Multiple Specialised Parsers
A single parser creates a single point of failure. Separate parsers handle each engineering artefact and emit defined schemas before outputs can be accepted.
parser_nameparser_versionsource_checksumproject_iddesign_revisiontool_versionmetric_value + unitlimitstatusevidence_locationwarnings
STA TEXT PARSER v2.4WNS = -0.083 nsSumber SHA-256 7F3A… · schema valid
STA TABLE VALIDATOR v1.8WNS = -0.038 nsSumber SHA-256 7F3A… · Konteks and Nilai Penyelarasan Failed
≠UNRESOLVED Both outputs preserved. Dependent high-risk actions stopped. Engineer review required.
Parser disagreement is an engineering signal, not an inconvenience to be hidden.
Independent Checks
Validate Outside the AI Model
Schema, units, ranges, IDs, PVT completeness, mathematics, statistics, versions, duplicate detection, evidence completeness, approved limits and policy are checked independently.
“Semua PVT corners passed.”
- Expected corners
- 27
- Parsed
- 26
- Passed
- 25
- Failed
- 1
- Missing
- 1
SchemaUnitsRangeRequirement IDsPVT completenessMathematical reconciliationStatistical consistencyVersi compatibilityCross-file comparisonDuplicatesMissing evidenceGolden flowApproved limitsPolicyManusia review
A fluent explanation is not engineering evidence.
Compute-Aware Orchestration
Right Tugasan. Right Model. Right Tenaga Kos.
Every task is classified by type, complexity, format, confidentiality, risk, required accuracy, latency, reproducibility, compute and energy impact.
Incoming Engineering TugasanTugasan · Risk · Data Class
Penghalaan priority1. Deterministic execution2. Existing validated result3. Cached approved result4. Specialised parser5. Small local model6. Model lanjutan only when necessary7. Manusia escalation for unresolved risk
Bounded Hasil States
Hallucination Must Be Contained Before It Reaches Engineering Decisions
Ketepatan prompting alone is insufficient. Approved retrieval, citations, schemas, numerical checks, parser comparison, keyakinan thresholds, contradictions, no-answer states, restricted permissions and immutable audit context reduce exposure.
VerifiedSupportedPartially SupportedConflicting EvidenceInsufficient EvidenceSemakan Manusia RequiredRejected by Validator
AI ANALYSIS STATUSPARTIALLY SUPPORTEDKeyakinan: medium · threshold for readiness: not met
- Evidence found
- 8 linked records · EP-R42-019
- Validator
- Evidence Gate v3.2 · audit RUN-1142
- Missing evidence
- Post-layout noise result · One PVT corner · Waiver approval
- Permitted action
- Generate review ringkasan
- Prohibited action
- Declare tape-out readiness
INSUFFICIENT VALIDATED EVIDENCE
Sumber-Level Kebolehkesanan
Every Penting Statement Must Point to Evidence
BOUNDED FINDING
Timing requirement REQ-TIM-014 is not satisfied.
Sumber file / locationSTA_Report_R42.txt · line 1842
Checksum7F3A…
ParserSTA Text Parser v2.4
Tool / PDKPrimeTime 2026.03 · PDK R19.2
Design revisionR42
WNS-0.083 ns
Limit≥ 0 ns
CornerSS / 0.81 V / 125°C
ValidationDeterministic parser confirmed
KeyakinanVerified extraction
Audit contextRUN-1142 · 2026-07-29 03:42 UTC
LimitationWaiver state pending
Accountable Sign-Off
Manusia Authority Remains Mandatory
AINNA supports engineering judgment. It does not replace engineering accountability.
Bantuan AI Analysis→Deterministic Validation→Evidence Ulasan→Berkelayakan Engineer→Design Authority→Final Kelulusan
ReviewerAssigned qualified engineer
PerananDesign Authority
TarikhPending review
Decision / conditionsNo approval recorded
Supporting evidenceEP-R42-019 · SHA-256 required
Kelulusan signatureCryptographic signature required
Tamat Tempoh / review dateRequired before release
Audit stateImmutable after signature
Seni Bina baselineRequirement changesDesign-rule waiversVerification waiversKebolehpercayaan exceptionsKeselamatan decisionsTape-out readinessProduction releaseKepatuhan statements
ESG-Aware Computing
Lagi Intelligence With Less Compute
Semiconductor innovation should not depend on sending every task to a large model. Detached services, local models, caching, task segmentation and smart routing reduce unnecessary AI execution.
10,000Incoming engineering tasks
6,200Rules, parsers and detached services
2,300Cached or validated results
1,100Tempatan small models
400Advanced reasoning models
Illustrative Seni Bina Example Not Measured Pelanggan Prestasi
Detached deterministic servicesSmall-model-first routingTempatan deploymentResult caching and reuseIncremental processingEvent-driven executionKelompok processingPengesanan duplikatRetrieval before generationParser-first architectureModel sleep and scale-downCompute-budget limitsCarbon-aware scheduling where availablePerkakasan utilisation monitoring
Estimation methodology and boundaries
Public figures use a 10,000-task illustrative baseline, classify deterministic and cached handling before model routing, and do not assume a production model, hardware platform, grid carbon factor or measured period. Actual telemetry must record model type, hardware, duration, energy source, cache policy, data transfer and keyakinan range.
ESG metrics must be calculated from actual deployment telemetry. Public website values are demonstration data unless explicitly supported by measured operational records.
Rendah-Power Tempatan Execution
Kedaulatan Data Controls the Route
Tempatan execution can reduce external transfer and latency while improving control over PDK, IP and compute usage.
Confidential PDK or proprietary IC data→Tempatan or customer-controlled environment only
Pelanggan on-premisesPeribadi VPCAir-gapped assessmentLLM Tempatan serverTempatan small modelsPelanggan-approved external modelHybrid routing
Reduced external data transferLower latencyImproved confidentialityControl over PDK and IP dataSmaller model selectionPredictable compute usageReduced hyperscale dependency
Luaran routing requires explicit customer approval, appropriate sanitisation, policy compliance, audit logging and an approved destination.
Seni Bina Operasi
Sistem Berasingan Papan Pemuka
Demonstration Data · Illustrative 10,000-task period · Updated 29 Jul 2026
Workload Distribution
Deterministic36%
Parser20%
Detached services / database6%
Cached results23%
Tempatan model11%
Model lanjutan4%
Kategori jumlah 100% and reconcile to the illustrative task funnel. Manusia review is tracked separately.Validation Status
Verified outputs8,412
Rejected outputs43
Parser conflicts17
Insufficient evidence84
Manusia review pending29
Policy-blocked actions12
Hallucination Containment
Unsupported claims blocked31
Missing citations52
Numerical inconsistencies14
Model contradictions9
Outputs escalated47
Prohibited actions prevented12
ESG Operasi
Model calls avoided8,500
Cache hit rate23%
Tempatan processing supportedYa
Compute reductionEstimated
Tenaga reductionEstimated
Data-transfer reductionEstimated
Run Konteks Integrity
Repeatable Semiconductor Engineering
Repeatability requires more than storing the final simulation report. A reproducible run must preserve the complete design, tool, model, script, configuration and computing context.
Project IDSENSOR-AFE-24
Design revisionC7
Git commit8c2f1e7
Perforce revisionCL 28419
IP versionafe_ip 3.4
PDK versionValidated input only
Model librarymodels_2026.07
EDA toolPelanggan-approved simulator
EDA / patch versionCaptured
Script versionnoise_run_v12
Testbench revisiontb_ana_42
Constraint filessha256 verified
Configuration filessha256 verified
PVT cornerSF / 0.81 V / 85°C
Monte Carlo seed842191
Solver settingsCaptured
Input checksums17 verified
Compute environmentcluster-a17
Operating systemEnterprise Linux
Run ownerANA-02
Timestamp2026-07-29 02:14 UTC
Expected outputInput-Referred Noise Density ≤ 12 nV/√Hz
Kelulusan statusUlasan required
Waiver statusNone
Visual demonstration only. No EDA simulation is executed.
Verification Intelligence
Trace the Requirement. Reproduce the Result. Tutup the Evidence.
Interactive demonstration data connects requirement intent to technical evidence and failure context.
Demonstration Data Not Pelanggan Prestasi
→→→→→→
Requirement revision Rev 4Owner PWR-03Status ApprovedAffected blocks PMIC, PORLinked tests 4Evidence files 7
REQ-ANA-0042 · Manifest MANIFEST-ANA-114
98.4%Yield1.82 mVMean Offset4.91 mV3σ Limit80Out-of-spec samples
923Regresi Failures
Top 4 regression rows account for 664 failures; 259 remaining failures are aggregated separately.
7Unique Signatures
7Root-Cause Dispositions
3Environment Issues
2RTL Issues
1Testbench Issue
1Unresolved
| Signature | Tests | First / Last | Suspected source | Owner | Status |
|---|---|---|---|---|---|
| SIG-CLK-07 | 312 | 02:11 / 06:42 | Environment | CAD-02 | Resolved |
| SIG-RST-03 | 184 | 02:14 / 06:39 | RTL | RTL-04 | Ulasan |
| SIG-TB-11 | 127 | 02:18 / 06:37 | Testbench | VER-07 | Fix queued |
| SIG-X-19 | 41 | 03:22 / 06:12 | Unresolved | VER-03 | Open |
Design for Six Sigma
Design for Six Sigma in Semiconductor Engineering
DMAIC improves existing engineering or manufacturing processes. DMADV structures baharu product and IC development.
Demonstration Data Not Pelanggan Prestasi
DMAIC
Define · Measure · Analyze · Improve · Control
DMADV
Define · Measure · Analyze · Design · Verify
- Voice of Pelanggan
- System and regulatory requirements
- PPA, cost and CTQs
- CTQ limits
- Existing IP capability
- Measurement uncertainty
- Defect escape baseline
- Seni Bina trade-off
- Sensitivity and DOE
- Design FMEA
- Risk prioritisation
- Robust design
- PVT and Monte Carlo
- DFT, DFM and reliability
- Verification strategy
- Requirements with linked tests
- DRC, LVS and PEX
- Silicon correlation
- Control plan
CTQ & Capability
Input-referred noise density≤ 12 nV/√HzSemasa 10.8 · Margin 10% · PASS
Cp 1.52Cpk 1.34Pp 1.41Ppk 1.2795% CI 1.18–1.36Kestabilan Ulasan
Capability indices are shown for this validated process window only and do not replace release criteria.
SPC & Measurement System
RepeatabilitytrackedReproducibilitytrackedBiastrackedKestabilantrackedLinearitytrackedGauge R&RtrackedTester correlationtrackedSimulasi-to-silicontracked
DOE & Design FMEA
AFE saturationKeterukan 8Occurrence 3Detection 4Owner ANA-02Evidence pending
Peranan-Aware Programme Intelligence
Engineering Dashboards
Evidence-backed tontonan for executives, engineering disciplines, repeatability, verification and silicon learning.
Semua Figures Are Demonstration Data
Seni Bina gate status trend
PPA trendmonitoredBlock readiness heatmapmonitoredEvidence freshness< 15 minGate blockers3 review
Total requirements trend
Evidence freshness< 15 minGate blockers3 review
PVT corner pass rate trend
CMRRmonitoredTemperature coefficientmonitoredStartup timemonitoredPost-layout degradationmonitoredSpecification marginmonitoredEvidence freshness< 15 minGate blockers3 review
Lint status trend
CongestionmonitoredDRCmonitoredRegresi pass ratemonitoredCode coveragemonitoredFunctional coveragemonitoredAssertion coveragemonitoredECO countmonitoredEvidence freshness< 15 minGate blockers3 review
Pelan completion trend
Reopened bugsmonitoredUnreproduced failuresmonitoredGate readinessmonitoredEvidence freshness< 15 minGate blockers3 review
Reproducible-run percentage trend
Reproduction failuresmonitoredEvidence completenessmonitoredEvidence freshness< 15 minGate blockers3 review
Wafer yield trend
Package correlationmonitoredSimulasi-to-silicon deltamonitoredFailure analysismonitoredField-return trendsmonitoredEvidence freshness< 15 minGate blockers3 review
PassMarginalFailNo data
Engineering Roles and Programmes
IC DesignOps by Peranan and Use Case
Select a role to see the workflow, dashboard and operational benefit most relevant to that team.
PRIMARY Aliran Kerja
Programme gate readiness
Sambung architecture, block status, verification closure and technical risk to current engineering evidence.
Papan Pemuka: Executive Programme
Benefit: Improved verification visibility and reduced manual coordination.
Analog front end
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Reduced manual coordination.
Power management IC
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Improved traceability.
Automotif sensor interface
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Lebih Pantas failure triage.
RISC-V subsystem
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Higher run reproducibility.
AI accelerator
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Better engineering knowledge retention.
Mixed-signal data converter
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Improved verification visibility.
RF front end
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Reduced manual coordination.
SerDes
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Improved traceability.
Keselamatan IP
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Lebih Pantas failure triage.
Chiplet
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Higher run reproducibility.
UCIe interface
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Better engineering knowledge retention.
Advanced packaging
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Improved verification visibility.
FPGA-to-ASIC migration
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Reduced manual coordination.
MEMS inertial sensor
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Improved traceability.
Sensor fusion
- Problem: Distributed requirements and inconsistent run context.
- Semasa workflow: Manual handoffs across alatan, reports and teams.
- AINNA workflow: Controlled manifest, trace links and evidence gates.
- Evidence: Run context, verification result, decision and approval.
- Expected outcome: Lebih Pantas failure triage.
Kedaulatan Data
Engineering Konteks Stays Inside the Approved Boundary
Public demonstrations and enterprise deployment are deliberately separated.
PUBLIC
Demonstration Environment
- Sanitised data
- Demonstration PDK context
- Public examples
- No proprietary IP
- No confidential customer designs
PRIVATE
Enterprise Environment
- Pelanggan-controlled infrastructure
- Peribadi VPC, on-premises or assessed air-gapped option
- PDK isolation and role-based access
- Encryption and audit-trail controls
- Tempatan or bring-your-own-model deployment option
- Controlled retention and deletion policies
Pelanggan IP Boundary
Public demonstrations use sanitised data. Production PDKs, proprietary IP, design databases and customer engineering information remain inside the customer-controlled environment. Pelaksanaan controls and air-gap suitability are validated per customer architecture, security policy and contract.
Open Authenticated Enterprise DemoTool-Agnostic Connectivity
Sambung the Engineering Konteks Around Existing Alat
Designed to connect through approved APIs, scripts, reports, schedulers, version-control systems and engineering data exports.
EDA & Engineering
CadenceSynopsysSiemens EDAAnsysCOMSOLKeysightMATLABSimulink
Versi Control
GitGitHubGitLabPerforce
Requirements & Lifecycle
PolarionJamaJiraIBM Engineering Requirements PengurusanPLM systems
Automasi & Compute
JenkinsGitLab CISlurmLSFKubernetesInternal job schedulers
Engineering Data
CSVJSONXMLREST APILogSimulasi reportsLiputan databasesTest dataWafer maps
Integration availability depends on tool licensing, customer environment, Akses API and approved security policies. Vendor names identify ecosystem context and do not imply certified integration.
Preserved Engineering Content
Engineering Sumber
The original semiconductor briefing and prompt material now supports the platform as practical engineering intake templates.
Engineering Prom Perpustakaan
Structured prompts for architecture, power, verification and review.
IC Design Briefing Rangka Kerja
Six controlled inputs: design type, implementation strategy, interface, power, validated tool context and required artefacts.
Analog Design Input Template
Specification, PVT, noise, device model, simulation and evidence fields.
Digital Design Input Template
RTL hierarchy, constraints, commit, owner, verification and sign-off context.
Verification Input Template
Pelan, tests, seeds, environment, coverage, waivers and defect context.
MEMS Input Template
Sensor mechanism, multiphysics context, interface, characterisation and calibration.
Tape-Out Ulasan Template
Evidence baseline, run manifests, DRC/LVS/PEX, waivers and human approvals.
Failure Analysis Template
Signature, affected tests, first occurrence, source hypothesis, owner and resolution evidence.
Controlled use
Do not place NDA-restricted PDK content, proprietary schematics, export-controlled data, customer requirements or unreleased IP into a public AI service. AINNA outputs are engineering drafts and evidence aids. PDK-aware operation requires validated, authorised inputs. Berkelayakan engineers retain design, verification and tape-out authority.
Scoped Evaluation
Mula a Controlled IC DesignOps Perintis
Begin with one bounded engineering workflow, agreed evidence boundaries and human-controlled success criteria.
