Bring full-flow autonomy to chip design with IC-star
The fastest path to better silicon. Then faster on every chip after.
Multiphysics-Aware Models
Design-Driven Model Navigator
IC Design Ontology
Reasoning Core
Four technologies work together to operationalize AI for semiconductor
01 / 04
Skip to benefits TRUSTED BY CHIP DESIGN TEAMS WORLDWIDE
AmbiqAndeseMemoryRambusWhalechipAmbiqAndeseMemoryRambusWhalechip
Benefits
- Full-flow autonomy in one platform
- You set the objective. Agents run the flow, from spec to verified result, digital and analog.
- Up to 10× faster PPA convergence
- Physical prediction and multi-objective optimization cut iterations and improve quality of results (QoR).
- Works with the tools you already run
- ChipAgents fits into your existing design environment, selecting models and tools by quality, latency and cost.
- Your IP never leaves your walls
- Keep proprietary design knowledge and execution context within customer-controlled infrastructure, with SOC 2 Type II controls and zero data retention.
- On-premises self-improvement
- Every run makes the next one smarter, on your hardware.
- Multi-domain collaboration
- Architects, RTL, verification and PD work from a shared context and platform.
“Over the past year, we have worked with ChipAgents to bring AI into our development workflows, creating a foundation that blends engineering knowledge with AI-powered assistance across the IC design lifecycle.”
“By adopting the ChipAgents platform, we see opportunities to accelerate the pace of chip and IP development, enhance engineering productivity, and strengthen our long-term position in AI and data-center infrastructure.”
“ChipAgents has gone beyond serving as an AI evaluator for eMemory’s behavioral model … It has also enabled our engineers to significantly enhance the quality and reliability of our IP datasheets.”
Questions about IC-star
Still have questions? Book a time with our team →
IC-star takes a single objective and plans, executes and verifies from engineering intent to verified outcomes, across digital and analog workflows, instead of automating one task in isolation.
IC-star deploys inside your environment, on-premise or in your own cloud tenant, so RTL, specs and reports never leave your perimeter. Access follows your existing project permissions, and your data is not used to train shared models.
Agent output is never trusted on its own. Each step is validated by the tools that already gate your flow (compilers, simulators, lint, formal, timing), and failed checks are fed back for another attempt. Physics-aware estimates guide exploration; your simulation, verification and implementation tools still validate every result. What reaches an engineer has passed the same evidence bar as hand-written work.
IC Design Ontology encodes your organizational design data, IP, specs and engineering know-how, so every decision is grounded in your context rather than generic patterns.
A generic model predicts text from public data. Silicon-specific agentic AI is built around the semiconductor flow itself: it reads your RTL, specs, constraints, waveforms and reports, calls your EDA tools, runs simulations, and checks its own results against physics and coverage data before reporting back. The difference is grounding and closure: generic models produce a suggestion, agents produce a verified outcome with the evidence attached.
Today teams run agents on RTL generation and refactoring, testbench and stimulus creation, coverage closure, regression triage and root-cause analysis, formal property generation, lint and CDC cleanup, timing and PPA exploration, analog sizing sweeps, and documentation of design intent. Anything with a machine-checkable definition of done is a good candidate; work that depends on unwritten judgment stays with engineers.
Engineers set the objective and the constraints; agents do the sweeps, retries and bookkeeping. Every run leaves a reviewable trail (what was changed, which tool was called, which check passed), so an engineer approves or redirects rather than re-deriving the work. In practice the role shifts from writing each iteration by hand to reviewing many iterations at once.
ChipAgents is proven in production across 3D IC (Whalechip), memory (eMemory) and edge AI (Ambiq) design flows. See customer stories for details.