- Reduce time-to-silicon by up to 10x through parallel AI-driven design exploration.
- Democratize advanced chip design for startups and mid-size companies competing with tier-1 firms.
- Eliminate tool-chain silos with a unified orchestration layer above major EDA vendors.
Technical Whitepaper · Version 1.0 · 2025
The Complete Agentic AI Solution for Semiconductor Design & Manufacturing
Qoresic presents itself as an AI-native IC design platform built around a central AI Supervisor Brain, a network of 19+ specialized agents, a shared semiconductor knowledge core, and integration across 24 EDA tool categories to orchestrate the full semiconductor workflow from specification through sign-off and manufacturing feedback.
Section 01
Executive summary
The whitepaper argues that the semiconductor industry has reached an inflection point because modern SoCs now span billions of transistors and dozens of IP blocks while traditional, siloed, expert-driven EDA workflows can no longer scale effectively to next-generation design demands.
- Continuously improve through a closed-loop learning system fed by real silicon results.
- Allow flexible deployment so customers can activate only the agents they need, independently or in coordinated pipelines.
- Position Qoresic as a smarter wrapper around existing EDA tools rather than a rip-and-replace platform.
“Qoresic does not replace your EDA tools — it makes them dramatically smarter, faster, and more accessible by wrapping them in collective AI intelligence.”
Section 02
The EDA crisis
The document describes a widening productivity gap in which the number of transistors that can be fabricated keeps rising, but engineering productivity and conventional design process evolution have lagged behind for decades.
| Challenge | Impact | Qoresic response |
|---|---|---|
| Siloed EDA point tools | Manual data transfer, errors, and lost context. | Unified AI agent orchestration layer. |
| Scarce domain experts | High cost, slow hiring, and knowledge loss. | 19+ AI expert agents available continuously. |
| Sequential design flow | Weeks of iteration at each stage. | Parallel agent collaboration reduces cycles. |
| Tool vendor lock-in | Forced reliance on a single-vendor ecosystem. | Vendor-agnostic integration spanning Synopsys, Cadence, Siemens, and more. |
| Knowledge fragmentation | Expertise siloed across teams and tools. | Shared Knowledge & Memory Core accessible to all agents. |
Section 03
The AI Supervisor Brain
At the center of the platform is the AI Supervisor Brain, described as an orchestration engine that reasons across the entire IC design domain, coordinates specialized agents, and continuously learns from every design run.
Activates the right agents at the right time and manages data flow and dependencies across the design pipeline.
Understands design intent from natural-language specifications and translates goals into actionable agent tasks.
Enriches the shared Knowledge Core after every design run so the system becomes smarter with each chip designed.
Dynamically adjusts agent strategies based on intermediate results, timing closure status, and power budgets.
Performs multi-objective optimization across power, performance, and area simultaneously.
Incorporates real-world fab feedback to close the loop between design and manufacturing.
Intelligence layer
AI Supervisor Brain as the central orchestration engine that reasons, plans, and coordinates all agents while translating business goals into technical actions.
Agent layer
19+ specialized AI agents functioning as domain experts across the entire IC flow, from specification and RTL to sign-off and physical verification.
Knowledge layer
Semiconductor Memory Core containing design knowledge, PDK data, silicon results, best practices, IP libraries, and trained AI models continuously updated by every design run.
Section 04
Complete AI agent ecosystem
The whitepaper says Qoresic deploys more than 19 specialized agents that can either operate independently as point solutions or collaborate in orchestrated pipelines for full-flow automation.
Specification Agent
Natural language understanding · intent extraction · constraint formalization · design-space definition.
Translates plain-English product requirements into formal design constraints, performance targets, and verification intent.
Architecture Agent
Design exploration · micro-architecture partitioning · PPA trade-off analysis · IP selection.
Explores thousands of architectural options in parallel and recommends optimal building blocks for the target application.
RTL / HLS Agent
RTL generation · HLS · code transformation · quality optimization.
Generates synthesizable RTL from architecture or translates algorithmic descriptions into optimized RTL.
Verification, DFT & Formal Agents
Simulation management · coverage analysis · scan insertion · ATPG · equivalence checking · CDC/RDC · property verification.
Together they automate verification closure, test generation, and exhaustive mathematical proof tasks that catch issues beyond conventional simulation alone.
Analog Synthesis Agent
Topology selection · component sizing · optimization across PVT corners.
Automates a traditionally manual analog synthesis process to reach robust margins under process, voltage, and temperature variation.
AMS / Understanding & Analog Layout Agents
SPICE management · PCell validation · behavioral modeling · matched placement · parasitic-aware layout.
Bridge analog-digital understanding and automate critical layout-dependent analog quality tasks.
Physical design agents
Floorplanning · P&R · CTS · extraction · power / SI analysis.
Optimize macro placement, routing, clocking, extraction, and integrity analysis while staying timing-aware throughout convergence.
Sign-off & security agents
STA · OCV/AOCV/POCV · reinforcement-learning optimization · aging · EM · TDDB · security and side-channel analysis.
Extend the platform into production-quality timing closure, reliability validation, and hardware security assurance.
Section 05
EDA tools integration across 24 categories
Qoresic is positioned as an orchestration layer above leading tools from Synopsys, Cadence, Siemens, Ansys, Keysight, and open-source alternatives while covering 24 categories from requirements capture to thermal and manufacturing data.
| # | Category | Representative integrated tools |
|---|---|---|
| 01–05 | Specification, architecture, RTL, formal, simulation | IBM DOORS Next, Stratus HLS, Design Compiler, Genus, Yosys, VC Formal, JasperGold, VCS, Xcelium, Questa, Verilator. |
| 06–10 | DFT, analog, SPICE/AMS, analog layout, floorplanning | TetraMAX, Modus, Virtuoso, Spectre, HSPICE, ADS, Q3D, ICC2, Innovus, Aprisa. |
| 11–18 | CTS, P&R, extraction, timing, power/SI/PI, signal integrity, physical verification, sign-off | ICC2-CTS, Innovus CTS, StarRC, QRC, PrimeTime, Tempus, RedHawk-SC, Voltus, HFSS, Sigrity, Calibre, PrimeECO, Conformal. |
| 19–24 | DFM, lithography, OPC, package & assembly, thermal / multiphysics, manufacturing data | DFMPro, YieldEnhancer, Proteus, OPC solutions, footprint libraries, Icepak, Mechanical, Flotherm, Simcenter. |
Section 06
Semiconductor Knowledge & Memory Core
The Knowledge & Memory Core is described as the shared intelligence backbone that stores semiconductor expertise in a continuously updated, universally accessible form rather than leaving it locked inside individuals or disconnected teams.
Captures design rules, architectural patterns, timing constraints, and best practices derived from successful chip designs.
Maintains verified and characterized IP blocks with performance models across process nodes for faster assembly from proven components.
Includes deep foundry knowledge spanning technology files, design rules, and process characterization from TSMC, Samsung, Intel Foundry, and others.
Feeds back measurement data, failure analysis, and yield learning from fabricated devices to close the design-to-silicon loop.
Aggregates methodologies and standards across IEEE, JEDEC, AUTOSAR, DO-254, and customer-specific requirements.
Stores models for timing prediction, congestion estimation, power modeling, and yield forecasting that can be continuously fine-tuned.
| Continuous data feed source | Content | Update frequency |
|---|---|---|
| Wafer fab data | Process parameters, lot data, yield metrics. | Per fab run. |
| IP & design reuse | New IP releases and characterization updates. | Continuous. |
| Process & PDK | PDK updates, new nodes, and rule changes. | Per PDK release. |
| Field / RMA and telemetry | Field failures, reliability data, application performance profiles, and power measurements. | Monthly to continuous depending on stream. |
Section 07
Agent orchestration modes
The platform defines three operating modes so customers can adopt AI assistance as a single specialist tool, a sequential pipeline, or a collaborative multi-agent environment sharing context through the Knowledge Core.
Mode 1: Solo AgentPoint solution deployment
Activate a single specialist agent for a focused task without engaging the full platform, which suits teams augmenting existing flows with targeted AI assistance.
Examples include timing analysis on an existing netlist, DFT insertion into completed RTL, power analysis on a block, or formal verification of a specific property.
Mode 2: Pipeline ModeSequential handoff
Agents run in sequence and pass outputs forward, mirroring the traditional EDA flow but with AI support at every stage to reduce iteration cycles.
Illustrative paths include RTL → Synthesis → P&R → Timing → Sign-off, or Specification → Architecture → RTL → Verification.
Mode 3: Collaborative ModeParallel multi-agent orchestration
Multiple agents work simultaneously, share design context through the Knowledge Core, and rely on the AI Brain to arbitrate conflicts and synthesize globally optimal recommendations.
Examples include power, SI, and thermal agents converging together, or timing, power, and area agents co-optimizing PPA targets under variation.
Section 08 & 09
Customer segments and competitive position
The whitepaper maps Qoresic to foundries, SoC companies, IC design houses, SiP and packaging teams, IDMs, and system design organizations, then distinguishes it from both traditional EDA and single-vendor AI features.
Use cases span yield improvement, architecture exploration, full digital and analog flows, package-level power integrity, thermal management, reliability assurance, hardware-software co-design, and end-product security certification.
Qoresic claims cross-vendor orchestration, modular agent selection, open ecosystem integration, and unified analog-plus-digital flow where traditional and single-vendor AI stacks remain partial or limited.
The document compares Qoresic to AWS for EDA by presenting it as the intelligence layer that abstracts complexity and democratizes access to world-class capability.
“Qoresic is to EDA tools what AWS is to data centers — the intelligent platform layer that abstracts complexity and democratizes access to world-class capability.”
Section 10
Roadmap and future vision
The roadmap moves from AI-assisted engineering to AI-led execution, then to natural-language-to-silicon workflows, and finally to self-improving silicon design driven by compounding cross-run learning.
Phase 1 — Now
AI agents augment human engineers with recommendations, automated analysis, and optimization while people still drive the flow and retain creative architecture decisions.
Declared capabilities include 19+ agents, 24 tool categories, solo/pipeline/collaborative modes, and an operational shared memory core.
Phase 2 — Near term
The AI Brain takes primary responsibility for execution while humans define intent and approve quality gates, compressing iteration cycles from weeks to hours.
Targets include natural-language-to-RTL generation, autonomous timing closure with approval gates, predictive yield optimization, and chiplet / 3D-IC coverage.
Phase 3 — Future
Product teams describe desired chip capabilities in plain language and the Brain autonomously architects, verifies, implements, and prepares the design for manufacture.
The file frames this as an “AWS moment” for semiconductors in which even software companies can design custom silicon.
Phase 4 — Long term
Each chip generation improves automatically as the knowledge core accumulates silicon data, design learning, and manufacturing feedback across many runs.
Long-term goals include anonymized cross-customer learning, automatic node migration, predictive zero-defect design targeting, and industry-wide productivity transformation.
Section 11
Conclusion and call to action
The whitepaper concludes that AI is not an incremental improvement to semiconductor design, but a fundamental reimagining of how chips are designed, and positions Qoresic as a comprehensive, flexible, intelligent, open, and accessible platform at the center of that shift.
- Comprehensive coverage from specification to sign-off across digital, analog, and mixed-signal domains.
- Flexible activation of independent agents or full-brain collaboration.
- Knowledge Core that compounds value with every design run.
- Open integration across major EDA vendors and open-source tools.
- Request a demo of a live multi-agent design flow through demo@qoresic.com.
- Join a pilot program to run a design block through Qoresic and measure PPA improvement via pilot@qoresic.com.
- Explore partnerships for EDA, foundry, and cloud integration at partners@qoresic.com.
Request a demo
See the AI Brain orchestrate a live design flow across multiple agents.
Pilot program
Run your next design block through Qoresic agents and measure PPA improvement.
Partnership
Explore co-innovation with EDA vendors, foundries, and cloud providers.
One Brain. Infinite Agents. Limitless Innovation.