Silicon Engineering Command Center

Repeatable IC Design.
Traceable Decisions.
Faster 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. Human-approved where critical.

Tool-AgnosticHuman Sign-OffPrivate DeploymentVersion ControlledRequirements TraceabilityAnalogDigitalMixed-SignalMEMSAdvanced Packaging

Explore NeuralOps Architecture Estimate NeuralOps Carbon Impact Explore AINNA ESG

Engineering-assisted, human-controlled. Final approval remains with qualified engineers.

RequirementSchematicRTLDRC reportLVS reportTiming reportPVT resultCoverage report

CONTROL LAYERAINNA NeuralOps
OrchestrationSanitise · Segment · Classify · Route

Rules + Parsers BYPASS AILocal Small Model LIGHTWEIGHTAdvanced Model ONLY IF REQUIRED

Independent ValidationEvidence PackEngineer Sign-Off

WaferDieFloorplanNeuralOpsHuman Authority

Lower Hallucination Exposure

Critical engineering results are parsed, validated and linked to evidence outside the AI model.

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 Data Sovereignty

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

Compare AI-assisted findings against independent evidence and validators.

Human Sign-Off

Keep architecture, waiver, verification and tape-out authority with engineers.

COMPUTE-EFFICIENT BY ARCHITECTUREFewer unnecessary model calls. Lower compute demand. More accountable semiconductor engineering.

Parser-First ProcessingSmall-Model-First RoutingGPU Only When RequiredCached Validated ResultsLocal Processing SupportedMeasurable Compute Telemetry

AINNA is the engineering intelligence, orchestration, traceability and repeatability layer around existing semiconductor design tools.

AINNA does not replace Cadence, Synopsys, Siemens EDA, Ansys, COMSOL or other semiconductor engineering tools. It connects requirements, workflows, results, decisions and engineering evidence across the IC development lifecycle.

Low-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

NO MODEL

Rules and Formula Engines

Timing limits · unit checks · PVT completeness

REUSE

Approved Databases and Cached Results

Requirements · validated baselines · prior evidence

CONTROLLED

Detached Engineering Services

DRC/LVS parsing · manifests · checksums · statistics

LOCAL

Local Small Models

Lightweight classification · metadata · document tagging

SELECTIVE

Advanced Reasoning Models

Complex comparison · ambiguity · synthesis · explanation

INDEPENDENT OUTPUT VALIDATIONEVIDENCE PACKQUALIFIED ENGINEER SIGN-OFF

Semiconductor-Specific Control Map

One Architecture. Six Engineering Domains.

Select a domain to see the bounded NeuralOps controls used around the engineering workflow.

NEURALOPS CONTROLS

Requirements Engineering

Requirement extraction

Requirement-ID validation

Ambiguity detection

Design-block mapping

Test mapping

Change-impact analysis

Missing-evidence detection

ESG-Aware Silicon Operations

Lower-Compute AI for Semiconductor Operations

Simulation 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 reuseMore local processingLower infrastructure demandMore 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.

GPU-FIRST AI ARCHITECTURE

Every report → Large model → Large context → Repeated generation → Manual checking

Higher compute demand
Greater hallucination surface
Weak reproducibility
Repeated token processing
More external data movement
Higher validation burden

AINNA NEURALOPS ARCHITECTURE

Report → Sanitise → Segment → Parse → Validate → Route → AI only where needed → Evidence → Human 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 Language

An estimated result can never be automatically presented as measured or independently verified.

LEVEL 1

Architecture Statement

NeuralOps is designed to reduce unnecessary model execution through smart routing, detached processing and specialised parsers.

Published architecture statementSource: AINNA NeuralOps architecture specification · Last reviewed: 2026-07-29

LEVEL 2

Estimated Projection

Estimated reduction based on configured workload, hardware, routing and grid assumptions.

Estimated Projection - Not a Certified Carbon AuditSource: AINNA Carbon Footprint Emulator configuration · Last reviewed: 2026-07-29Open Carbon Footprint Emulator

LEVEL 3

Measured Operational Result

Publication blocked until this evidence level passes its governance and approval requirements.

Not available for publicationSource: Production telemetry - not yet connected · Last reviewed: 2026-07-29

LEVEL 4

Verified Independent Claim

Publication blocked until this evidence level passes its governance and approval requirements.

Not available for publicationSource: Independent assurance - not yet available · Last reviewed: 2026-07-29

Operational 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_range

FUTURE-READY COMPONENT

NeuralOps Compute and Carbon Evidence Pack

Demonstration DataEstimatedOperationally MeasuredInternally ReviewedIndependently Verified

Measurement periodDefined workloadAI-heavy baselineNeuralOps routing distributionDetached-system workloadParser workloadLocal-model workloadAdvanced-model workloadModel calls avoidedCache hit rateGPU runtimeCPU runtimeEstimated or measured energyEnergy source or meter identityEvidence checksumPUE assumptionGrid-emission factorEstimated carbon footprintBaseline carbon footprintReduction estimateCalculation methodologyExcluded workloadsConfidence and uncertainty rangeAssumptionsLimitationsData completenessApproval recordVerification status

Semiconductor ESG and Compute Dashboard

Route-Level Compute Evidence

DEMONSTRATION DATA — NOT MEASURED CUSTOMER PERFORMANCE

ProjectDesign stageEngineering domainProcessing routeModelHardwareMeasurement period

Total Engineering Tasks10,000

Deterministic Processing Rate36%

Specialised Parser Rate20%

Detached Services / Database Rate6%

Local-Model Rate11%

Advanced-Model Rate4%

Model Calls AvoidedDemo only

Cache Hit Rate23%

GPU RuntimeDemo only

CPU RuntimeDemo only

Data Transfer AvoidedEstimated

Estimated Energy UsageNot calculated

Estimated Carbon FootprintNot calculated

Baseline ComparisonConfigure baseline

Estimated ReductionNot calculated

Evidence CompletenessArchitecture only

Model the Architecture

Compare AI-Heavy and Smart-Routed Processing

Configure workload, hardware, routing, PUE and grid assumptions before making an estimated projection.

Estimate NeuralOps Carbon Impact Explore AINNA ESG

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. Review assumptions in the Carbon Footprint 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, tools, models, scripts, seeds and environments drift.

Operational impact: Results become difficult to reproduce or compare.
AINNA control: Captures a complete, checksum-aware Run Manifest.

Weak Requirements Traceability

Requirements are not consistently connected to blocks, tests and evidence.

Operational impact: Change 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: Regression triage consumes senior engineering time.
AINNA control: Clusters failure signatures and exposes closure blockers.

Limited Management 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 Projects

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 Noise < 12 nV/√Hz

Operating Temperature: -40°C to 125°C

Verification Required

maps to

DESIGN BLOCKAnalog Front EndOwner: ANA-02 · Rev C7

Sensor→

Analog Front End→

ADC→

Digital Processing→

Interface

Signal intent and interface assumptions remain linked to the requirement baseline.

ANALOG CONTEXT

Low-Noise AFE

Schematic: afe_top_C7
Device parameters: verified
PDK reference: validated input required
Simulation 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;

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 coverage86%

Regression pass rate93%

Open defects14

Waivers7

Regression failures are grouped into unique signatures before engineering review.

CPU

SRAM

AFE

NoC

I/O

Placement CompleteClock tree CleanCongestion 2 hotspotsIR drop ReviewDRC 2 markersTiming WNS +0.03ns

87%Tape-Out Readiness

RequirementslinkedDesign revisionlinkedSimulation resultslinkedRun manifestslinkedCoveragelinkedWaiverslinkedFMEAlinkedSign-off checklistlinkedHuman approvalslinked

Controlled Engineering System

The AINNA IC DesignOps Platform

Modules 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
Change-impact analysis
Missing-test detection
Approval workflow

MODULE 02

Engineering Knowledge Vault

Approved design patterns
Architecture 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
Risks and approver
Invalidating conditions

MODULE 04

Golden Flow Registry

Analog simulation
RTL verification and synthesis
Timing and physical design
DRC, LVS and PEX
Reliability and MEMS
Tape-out and characterisation

OwnerVersionTool / PDK compatibilityValidation evidenceStatus

MODULE 05

Verification Intelligence

Verification-plan tracking
Regression monitoring
Failure clustering
Coverage closure
Waiver and defect aging
Readiness gates

MODULE 06

IP Reuse Intelligence

Qualified 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
Coverage, waivers and FMEA
CTQ and known limitations
Human 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.

Use advanced AI only when advanced intelligence is genuinely required.

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 checksVersion-control checksDatabase queriesApproved workflow enginesDetached servicesSmall local models where appropriate

Activate advanced reasoning selectively

Advanced models are reserved for work where interpretation and synthesis add genuine engineering value.

Complex reasoningCross-document interpretationAmbiguity analysisEngineering comparisonRoot-cause hypothesesKnowledge synthesisHuman-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.

AI MAY ASSIST

Classification
Summarisation
Engineering knowledge retrieval
Failure clustering
Documentation
Requirements interpretation
Workflow recommendations
Risk identification
Engineering comparison
Draft analysis

AI MUST NOT INDEPENDENTLY

Approve tape-out
Modify a production PDK
Change sign-off constraints
Approve a waiver
Release confidential design data
Declare a design compliant
Declare a verification result valid
Alter approved engineering evidence
Execute destructive production actions
Replace qualified engineer approval

01Engineering Input

02Security Boundary

03Sanitisation

04Segmentation

05Data Classification

06Specialised Parser

07Deterministic Validation

08Smart Routing

09Detached / Approved AI

10Independent Validator

11Evidence Pack

12Human Sign-Off

Stage 1 of 12: Engineering document enters the controlled boundary.

Detached and Deterministic Processing96%

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 Services

Detached Systems

Detached Systems are engineering services that operate independently from the language model. They execute controlled, repeatable and auditable tasks without depending on AI-generated reasoning.

The AI model is not responsible for validating its own output.

CONVENTIONAL AI-FIRST

Engineering Input → Large AI Model → AI Interpretation → AI-Generated Result → Engineer Reviews Everything

Unnecessary AI dependency
High compute usage
Difficult repeatability
Increased hallucination surface
Weak evidence separation

AINNA DETACHED WORKFLOW

Input → Sanitisation → Segmentation → Parser → Validation → Detached Services → AI only when required → Human Approval

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 ingestionCoverage calculationRegression statusDRC / LVS parsingPVT matrix generationMonte Carlo aggregationStatistical calculationsCapability indicesWafer-map generationEvidence-pack assemblyPermission checksRetention enforcementAudit-log generationApproval-state management

Decompose Before Reasoning

Segmentation Before Intelligence

A request such as “Review 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 tools, 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
Compare against approved limits
Identify missing evidence
Generate technical summary
Route unresolved issues to engineers

Deterministic

File validation, numeric calculation, checksum, lookup

AI not permitted or unnecessary

Low-Risk Assisted

Classification, metadata, formatting, basic summary

Parser or small local model preferred

Engineering Analysis

Ambiguity, failure clustering, cross-report comparison

Evidence-grounded model permitted

High-Risk Decision Support

Waiver, sign-off, safety or reliability analysis

Independent validation and human approval mandatory

Prohibited Autonomous Action

Tape-out, PDK change, production release, destructive action

Autonomous execution prohibited

SEGMENT 09 · APPROVED-LIMIT COMPARISON

Defined inputNormalised timing metrics + REQ-TIM baseline

Output schemarequirement_id · value · unit · limit · status

Allowed toolsRule engine + approved-limit database

Risk / data classL0 deterministic · Confidential IC

Validation / evidenceUnit check + baseline checksum

Autonomy / approvalCompare only · engineer approves disposition

Untrusted by Default

Sanitise Before Processing

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 FileFile + MIME ValidationMalware / Archive CheckContent ExtractionPrompt-Injection DetectionConfidentiality ClassificationMetadata NormalisationApproved Processing 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 inspectionExternal-link validationSecret and credential detectionPDK and proprietary IP detectionPersonal-data detectionCustomer-project isolationPrivate-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 nsSource SHA-256 7F3A… · schema valid

STA TABLE VALIDATOR v1.8WNS = -0.038 nsSource SHA-256 7F3A… · maths check 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.

AI STATEMENT

“All PVT corners passed.”

INDEPENDENT VALIDATOR

Expected corners: 27
Parsed: 26
Passed: 25
Failed: 1
Missing: 1

FINAL STATUSAI STATEMENT REJECTEDConflicting and missing deterministic evidence

SchemaUnitsRangeRequirement IDsPVT completenessMathematical reconciliationStatistical consistencyVersion compatibilityCross-file comparisonDuplicatesMissing evidenceGolden flowApproved limitsPolicyHuman review

A fluent explanation is not engineering evidence.

Compute-Aware Orchestration

Right Task. Right Model. Right Energy Cost.

Every task is classified by type, complexity, format, confidentiality, risk, required accuracy, latency, reproducibility, compute and energy impact.

Incoming Engineering TaskTask · Risk · Data Class

Structured Calculation→Deterministic EngineReport Extraction→Specialised ParserSimple Classification→Local Small ModelCross-Domain Analysis→Advanced Reasoning ModelHigh-Risk Decision→Engineer Review

Routing priority1. Deterministic execution2. Existing validated result3. Cached approved result4. Specialised parser5. Small local model6. Advanced model only when necessary7. Human escalation for unresolved risk

Bounded Output States

Hallucination Must Be Contained Before It Reaches Engineering Decisions

Accuracy prompting alone is insufficient. Approved retrieval, citations, schemas, numerical checks, parser comparison, confidence thresholds, contradictions, no-answer states, restricted permissions and immutable audit context reduce exposure.

VerifiedSupportedPartially SupportedConflicting EvidenceInsufficient EvidenceHuman Review RequiredRejected by Validator

AI ANALYSIS STATUSPARTIALLY SUPPORTEDConfidence: 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 summary
Prohibited action: Declare tape-out readiness

INSUFFICIENT VALIDATED EVIDENCE

Source-Level Traceability

Every Important Statement Must Point to Evidence

BOUNDED FINDING

Timing requirement REQ-TIM-014 is not satisfied.

Source 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

ConfidenceVerified extraction

Audit contextRUN-1142 · 2026-07-29 03:42 UTC

LimitationWaiver state pending

Accountable Sign-Off

Human Authority Remains Mandatory

AINNA supports engineering judgment. It does not replace engineering accountability.

AI-Assisted AnalysisDeterministic ValidationEvidence ReviewQualified EngineerDesign AuthorityFinal Approval

ReviewerAssigned qualified engineer

RoleDesign Authority

DatePending review

Decision / conditionsNo approval recorded

Supporting evidenceEP-R42-019 · SHA-256 required

Approval signatureCryptographic signature required

Expiry / review dateRequired before release

Audit stateImmutable after signature

Architecture baselineRequirement changesDesign-rule waiversVerification waiversReliability exceptionsSafety decisionsTape-out readinessProduction releaseCompliance statements

ESG-Aware Computing

More 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

1,100Local small models

400Advanced reasoning models

Illustrative Architecture Example — Not Measured Customer Performance

Detached deterministic servicesSmall-model-first routingLocal deploymentResult caching and reuseIncremental processingEvent-driven executionBatch processingDuplicate detectionRetrieval before generationParser-first architectureModel sleep and scale-downCompute-budget limitsCarbon-aware scheduling where availableHardware utilisation monitoring

Total engineering tasks10,000

Tasks without LLM85%

Small-model tasks11%

Advanced-model tasks4%

Avoided model calls8,500

Estimated compute avoidedDemo only

Estimated energy reductionDemo only

Estimated carbon reductionDemo only

Cache hit rate23%

Local processing rate96%

Data-transfer reductionEstimated

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 confidence range.

ESG metrics must be calculated from actual deployment telemetry. Public website values are demonstration data unless explicitly supported by measured operational records.

Low-Power Local Execution

Data Sovereignty Controls the Route

Local execution can reduce external transfer and latency while improving control over PDK, IP and compute usage.

Confidential PDK or proprietary IC data→Local or customer-controlled environment only

Customer on-premisesPrivate VPCAir-gapped assessmentLocal LLM serverLocal small modelsCustomer-approved external modelHybrid routing

Reduced external data transferLower latencyImproved confidentialityControl over PDK and IP dataSmaller model selectionPredictable compute usageReduced hyperscale dependency

External routing requires explicit customer approval, appropriate sanitisation, policy compliance, audit logging and an approved destination.

Architecture Operations

Detached Systems Dashboard

Demonstration Data · Illustrative 10,000-task period · Updated 29 Jul 2026

Workload Distribution

Deterministic36%

Parser20%

Detached services / database6%

Cached results23%

Local model11%

Advanced model4%

Categories total 100% and reconcile to the illustrative task funnel. Human review is tracked separately.

Validation Status

Verified outputs8,412

Rejected outputs43

Parser conflicts17

Insufficient evidence84

Human review pending29

Policy-blocked actions12

Hallucination Containment

Unsupported claims blocked31

Missing citations52

Numerical inconsistencies14

Model contradictions9

Outputs escalated47

Prohibited actions prevented12

ESG Operations

Model calls avoided8,500

Cache hit rate23%

Local processing96%

Compute reductionEstimated

Energy reductionEstimated

Data-transfer reductionEstimated

Run Context 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 toolCustomer-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 noise spectrum

Accepted tolerance≤ 12 nV/√Hz

Approval statusReview required

Waiver statusNone

Visual demonstration only. No EDA simulation is executed.

Verification Intelligence

Trace the Requirement. Reproduce the Result. Close the Evidence.

Interactive demonstration data connects requirement intent to technical evidence and failure context.

Demonstration Data — Not Customer Performance

Requirement revision Rev 4Owner PWR-03Status ApprovedAffected blocks PMIC, PORLinked tests 4Evidence files 7

REQ-ANA-0042 · Manifest MANIFEST-ANA-114

98.4%Estimated Yield1.82 mVMean Offset4.91 mV3σ Limit17Outliers

923Regression Failures

7Unique Signatures

3Root Causes

2Environment Issues

1RTL Issue

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	Review
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 new product and IC development.

Demonstration Data — Not Customer Performance

DMAIC

Define · Measure · Analyze · Improve · Control

DMADV

Define · Measure · Analyze · Design · Verify

Voice of Customer
System and regulatory requirements
PPA, cost and CTQs

CTQ baseline

CTQ limits
Existing IP capability
Measurement uncertainty
Defect escape baseline

Measurement plan

Architecture trade-off
Sensitivity and DOE
Design FMEA
Risk prioritisation

Trade study

Robust design
PVT and Monte Carlo
DFT, DFM and reliability
Verification strategy

Design evidence

Requirement coverage
DRC, LVS and PEX
Silicon correlation
Control plan

Sign-off package

CTQ & Capability

Input noiseTarget 12 nV/√HzCurrent 10.8 · Margin 10% · PASS

Cp 1.52Cpk 1.34Pp 1.41Ppk 1.2795% CI 1.18–1.36Stability Review

Capability indices should only be interpreted after validating process stability, sampling suitability and data distribution.

SPC & Measurement System

RepeatabilitytrackedReproducibilitytrackedBiastrackedStabilitytrackedLinearitytrackedGauge R&RtrackedTester correlationtrackedSimulation-to-silicontracked

DOE & Design FMEA

Bias currentDevice widthCompensation CTemperatureInteraction A×B

AFE saturationSeverity 8Occurrence 3Detection 4Owner ANA-02Evidence pending

Role-Aware Programme Intelligence

Engineering Dashboards

Evidence-backed views for executives, engineering disciplines, repeatability, verification and silicon learning.

All Figures Are Demonstration Data

Architecture gate82%

Design completion15

Verification closure+5.2%

Tape-out readiness10

Critical risks86%

Schedule variance19

Engineering workload+9.2%

Compute utilisation14

Architecture gate trend

PPA trendmonitoredBlock readiness heatmapmonitoredEvidence freshness< 15 minGate blockers3 review

LotWaferDieTest binTemperatureTesterPackageDate

PassMarginalFailNo data

Engineering Roles and Programmes

IC DesignOps by Role and Use Case

Select a role to see the workflow, dashboard and operational benefit most relevant to that team.

PRIMARY WORKFLOW

Programme gate readiness

Connect architecture, block status, verification closure and technical risk to current engineering evidence.

Dashboard: Executive Programme

Benefit: Improved verification visibility and reduced manual coordination.

Analog front end

Problem: Distributed requirements and inconsistent run context.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, reports and teams.
AINNA workflow: Controlled manifest, trace links and evidence gates.
Evidence: Run context, verification result, decision and approval.
Expected outcome: Improved traceability.

Automotive sensor interface

Problem: Distributed requirements and inconsistent run context.
Current workflow: Manual handoffs across tools, reports and teams.
AINNA workflow: Controlled manifest, trace links and evidence gates.
Evidence: Run context, verification result, decision and approval.
Expected outcome: Faster failure triage.

RISC-V subsystem

Problem: Distributed requirements and inconsistent run context.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, reports and teams.
AINNA workflow: Controlled manifest, trace links and evidence gates.
Evidence: Run context, verification result, decision and approval.
Expected outcome: Improved traceability.

Security IP

Problem: Distributed requirements and inconsistent run context.
Current workflow: Manual handoffs across tools, reports and teams.
AINNA workflow: Controlled manifest, trace links and evidence gates.
Evidence: Run context, verification result, decision and approval.
Expected outcome: Faster failure triage.

Chiplet

Problem: Distributed requirements and inconsistent run context.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, 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.
Current workflow: Manual handoffs across tools, reports and teams.
AINNA workflow: Controlled manifest, trace links and evidence gates.
Evidence: Run context, verification result, decision and approval.
Expected outcome: Faster failure triage.

Data Sovereignty

Engineering Context 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

Customer-controlled infrastructure
Private VPC, on-premises or assessed air-gapped option
PDK isolation and role-based access
Encryption and audit-trail controls
Local or bring-your-own-model deployment option
Controlled retention and deletion policies

Customer IP Boundary

Public demonstrations use sanitised data. Production PDKs, proprietary IP, design databases and customer engineering information remain inside the customer-controlled environment. Deployment controls and air-gap suitability are validated per customer architecture, security policy and contract.

Open Authenticated Enterprise Demo

Tool-Agnostic Connectivity

Connect the Engineering Context Around Existing Tools

Designed to connect through approved APIs, scripts, reports, schedulers, version-control systems and engineering data exports.

EDA & Engineering

CadenceSynopsysSiemens EDAAnsysCOMSOLKeysightMATLABSimulink

Version Control

GitGitHubGitLabPerforce

Requirements & Lifecycle

PolarionJamaJiraIBM Engineering Requirements ManagementPLM systems

Automation & Compute

JenkinsGitLab CISlurmLSFKubernetesInternal job schedulers

Engineering Data

CSVJSONXMLREST APILogsSimulation reportsCoverage databasesTest dataWafer maps

Integration availability depends on tool licensing, customer environment, API access and approved security policies. Vendor names identify ecosystem context and do not imply certified integration.

Preserved Engineering Content

Engineering Resources

The original semiconductor briefing and prompt material now supports the platform as practical engineering intake templates.

RESOURCE 01

Engineering Prompt Library

Structured prompts for architecture, power, verification and review.

RESOURCE 02

IC Design Briefing Framework

Six controlled inputs: design type, implementation strategy, interface, power, validated tool context and required artefacts.

RESOURCE 03

Analog Design Input Template

Specification, PVT, noise, device model, simulation and evidence fields.

RESOURCE 04

Digital Design Input Template

RTL hierarchy, constraints, commit, owner, verification and sign-off context.

RESOURCE 05

Verification Input Template

Plan, tests, seeds, environment, coverage, waivers and defect context.

RESOURCE 06

MEMS Input Template

Sensor mechanism, multiphysics context, interface, characterisation and calibration.

RESOURCE 07

Tape-Out Review Template

Evidence baseline, run manifests, DRC/LVS/PEX, waivers and human approvals.

RESOURCE 08

Failure Analysis Template

Signature, affected tests, first occurrence, source hypothesis, owner and resolution evidence.

ENGINEERING TEMPLATE

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. Qualified engineers retain design, verification and tape-out authority.

Scoped Evaluation

Start a Controlled IC DesignOps Pilot

Begin with one bounded engineering workflow, agreed evidence boundaries and human-controlled success criteria.

Name Company Role Work email Semiconductor segment Design type Current EDA environment Preferred deployment Main engineering problem Pilot objective I understand this public form must not contain confidential designs, production PDK content or proprietary engineering files, and I consent to AINNA storing this pilot request for follow-up.

Repeatable IC Design.Traceable Decisions.Faster Verification.

Lower Hallucination Exposure

Higher Engineering Repeatability

Lower Compute Demand

Stronger Data Sovereignty

Sanitise

Segment

Parse

Validate

Route

Assist

Verify

Human Sign-Off

NeuralOps for Efficient and Governed Silicon Engineering

Rules and Formula Engines

Approved Databases and Cached Results

Detached Engineering Services

Local Small Models

Advanced Reasoning Models

One Architecture. Six Engineering Domains.

Requirements Engineering

Analog and Mixed-Signal Design

Digital and RTL Design

Physical Design

Verification

Product, Test and Yield

Lower-Compute AI for Semiconductor Operations

Evidence Level Controls the Language

Architecture Statement

Estimated Projection

Measured Operational Result

Verified Independent Claim

Semiconductor Compute Telemetry

NeuralOps Compute and Carbon Evidence Pack

Route-Level Compute Evidence

Compare AI-Heavy and Smart-Routed Processing

Semiconductor Engineering Problems We Solve

Fragmented Design Knowledge

Non-Repeatable Engineering Runs

Weak Requirements Traceability

Slow Verification Closure

Limited Management Visibility

Lost Learning Between Projects

From Requirements to Silicon Evidence

Low-Noise AFE

Control RTL

The AINNA IC DesignOps Platform

Requirements Intelligence

Engineering Knowledge Vault

Design Decision Records

Golden Flow Registry

Verification Intelligence

IP Reuse Intelligence

Design Evidence Pack

AI Must Never Become a Single Point of Engineering Failure

Keep validation-critical work deterministic

Activate advanced reasoning selectively

Detached Systems

Segmentation Before Intelligence

Sanitise Before Processing

Multiple Specialised Parsers

Validate Outside the AI Model

Right Task. Right Model. Right Energy Cost.

Hallucination Must Be Contained Before It Reaches Engineering Decisions

Every Important Statement Must Point to Evidence

Timing requirement REQ-TIM-014 is not satisfied.

Human Authority Remains Mandatory

More Intelligence With Less Compute

Data Sovereignty Controls the Route

Detached Systems Dashboard

Workload Distribution

Validation Status

Hallucination Containment

ESG Operations

Repeatable Semiconductor Engineering

Trace the Requirement. Reproduce the Result. Close the Evidence.

Design for Six Sigma in Semiconductor Engineering

CTQ & Capability

SPC & Measurement System

DOE & Design FMEA

Repeatable IC Design.
Traceable Decisions.
Faster Verification.