Overview
Moores Lab AI is an AI-powered engineering platform for semiconductor and chip design workflows. It focuses on using agentic AI to help engineers accelerate architecture exploration, RTL design, functional verification, simulation debug, regression triage, and coverage closure.
The platform should be understood as a productivity layer that works alongside existing semiconductor design and EDA tools. It does not replace signoff tools, human engineering judgment, or formal design reviews. Instead, it helps teams move faster by generating drafts, summarizing complex artifacts, identifying likely issues, and automating repetitive analysis.
Core Use Cases
Architecture and Specification Analysis
Moores Lab AI can help parse architecture documents, microarchitecture specifications, interface definitions, register maps, and protocol descriptions. It can extract requirements, identify ambiguities, summarize expected behavior, and convert written specifications into structured engineering tasks.
Typical outputs include requirement tables, interface summaries, verification objectives, traceability maps, and lists of open questions.
RTL Design Assistance
For RTL designers, the platform can assist with module scaffolding, interface generation, state machine drafts, code review, and design-rule recommendations. Generated RTL should always be treated as a draft and must be reviewed, compiled, linted, and validated before use.
Verification Planning
Moores Lab AI can help verification engineers create verification plans from specifications. It can suggest test scenarios, coverage points, assertions, legal and illegal stimulus cases, and priority areas for testing.
The goal is not to replace verification planning, but to reduce manual setup time and help engineers avoid missing important scenarios.
UVM and Testbench Development
The platform can assist with UVM testbench components such as agents, drivers, monitors, scoreboards, sequences, and configuration objects. It may also suggest reusable testbench patterns based on project methodology.
All generated verification code must be reviewed for correctness, methodology compliance, simulator compatibility, and alignment with design intent.
Regression Triage and Debug
Moores Lab AI can analyze simulation logs, regression results, failure signatures, recent commits, and test metadata. It can cluster failures, summarize likely root causes, recommend reproduction commands, and suggest next debug steps.
A typical regression triage output should include failing tests, shared error signatures, supporting evidence, suspected owners, confidence level, and recommended actions.
Coverage Closure
The platform can help analyze coverage reports and identify gaps. It can map uncovered bins to requirements, suggest new tests, recommend constraint changes, and highlight possibly unreachable scenarios.
Coverage exclusions should never be applied automatically. They require engineering justification and approval.
Reference Architecture
Moores Lab AI can be organized into five layers:
- User interface layer: web app, IDE plugin, CLI, API, or chat workspace.
- Agent orchestration layer: planner, task router, tool caller, evaluator, and memory.
- Semiconductor AI layer: spec, RTL, verification, debug, regression, and coverage agents.
- Integration layer: EDA tools, simulators, Git, CI/CD systems, issue trackers, and artifact stores.
- Data and governance layer: specs, RTL, logs, coverage databases, permissions, policies, and audit records.
Data Inputs
Common inputs include architecture specifications, RTL files, SystemVerilog code, UVM testbenches, assertions, simulation logs, regression results, waveform metadata, coverage reports, Git history, pull requests, issue tickets, and internal documentation.
Data Outputs
Common outputs include verification plans, test plans, RTL drafts, UVM templates, assertion candidates, debug summaries, regression triage reports, coverage gap reports, review comments, and engineering documentation.
Security and IP Protection
Semiconductor projects contain sensitive intellectual property. Any deployment must enforce access control, encryption, audit logging, data retention policies, tenant isolation, and clear rules about whether customer data can be used for model improvement.
Recommended safeguards include role-based access control, single sign-on, private artifact storage, secrets management, and human approval before generated code is committed.
Human Review Policy
AI-generated artifacts are engineering drafts. Engineers must review all outputs before they are merged, executed in production flows, or used for signoff decisions.
Reviewers should check functional correctness, protocol compliance, reset behavior, timing assumptions, clock-domain assumptions, security implications, coverage intent, assertion validity, and coding standard compliance.
Engineering Guardrails
Generated RTL must compile and pass lint checks. Generated verification code must be simulator-compatible. Assertions must be reviewed for false positives and false negatives. Debug conclusions must cite evidence from logs, waveforms, commits, or coverage data. Coverage recommendations must be tied to requirements.
Deployment Options
Moores Lab AI may be deployed in cloud, on-premises, or hybrid environments. Cloud deployment supports faster onboarding and elastic scaling. On-premises deployment is better for highly sensitive IP. Hybrid deployment can combine centralized management with local handling of confidential design artifacts.
Success Metrics
Key metrics include time saved per workflow, acceptance rate of generated artifacts, regression triage accuracy, compile success rate for generated code, coverage recommendation usefulness, human review pass rate, failed agent runs, and user satisfaction.
Adoption Roadmap
Start with read-only workflows such as documentation summarization, spec parsing, regression summaries, and coverage summaries. Next, enable draft generation for verification plans, UVM skeletons, assertions, and tests. Later phases can integrate with CI/CD, Git, issue trackers, and governed automation with approval gates and audit records.
Minimum Production Checklist
Before production use, confirm that access controls, audit logging, data retention, EDA integrations, CI/CD connections, prompt templates, evaluation benchmarks, human review gates, and rollback processes are in place.
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