SFA Semicon builds AI-powered electronic design automation (EDA) — the software that turns ideas into manufacturable chips — and uses it to design custom AI silicon, optimise models for hardware and train the engineers of tomorrow.
The next decade of computing will be defined by chips built for AI — and by AI that builds chips. SFA Semicon sits at that intersection.
We are part of the SFA Tech group, which has delivered technology solutions and resource training since 2015. SFA Semicon takes that experience into silicon, in step with the vision of Make in India and India’s growing semiconductor ecosystem.
Our architects, verification engineers, physical designers and ML scientists work as one team, using machine learning at every stage of the flow to deliver silicon that is faster, more efficient and ready sooner.
Machine learning guides architecture exploration, verification and physical design so teams converge faster.
From algorithm and RTL through GDSII, packaging, bring-up and production test.
Engineering from Bhopal, India, with a trusted partner network across the major semiconductor hubs.
Our academy builds job-ready VLSI and AI-hardware engineers for us and our partners.
EDA is the software that designs modern chips. Our AI-powered flow automates every step between an idea and the files a foundry needs — scroll to watch a design come together.
Engineers describe what the chip should do in a hardware description language. Our LLM copilots draft, lint and review RTL as it is written.
RTL lint · 0 errorsBillions of cycles are simulated to prove the design behaves exactly as specified. AI ranks and prunes regressions so the bugs surface first.
Functional coverage 98.7%RTL is synthesised into a netlist of standard-cell logic gates, and test structures are inserted so every chip can be screened in production.
1.2M cells mappedMillions of cells are placed and wired across a dozen metal layers. Reinforcement-learning agents explore floorplans a human team would never have time to try.
Wirelength −14% vs. baselineTiming, power, reliability and manufacturing rules are checked across every process corner, and hotspots are fixed before a single wafer is committed.
WNS +12 ps · DRC 0The finished layout is exported as GDSII — the geometric blueprint the foundry uses to make photomasks and manufacture the chip.
GDSII ready for the foundryTools, flows and expert services covering the full design cycle — use them end to end, or plug them into the flow you already run.
Model a system-on-chip before any RTL exists. Transaction-level simulation predicts performance, power and bandwidth, so the big trade-offs are settled early and cheaply.
Explores thousands of architecture variants and proposes the ones that hit your PPA targets.
Digital, analog and mixed-signal simulation, formal proofs and hardware-assisted emulation that catch functional bugs long before silicon.
Ranks tests by their chance of finding new bugs and auto-groups failures by root cause.
Turn RTL into an optimised gate-level netlist, then build in scan chains, memory BIST and boundary scan for high-coverage manufacturing test.
Machine-learned synthesis settings that improve with every run of your design.
Floorplanning, placement, clock-tree synthesis and routing for digital blocks, plus constraint-driven layout for analog and custom circuits.
Agents that learn to place macros and relieve congestion faster than manual iteration.
Static timing, power, IR-drop, electromigration, thermal and signal-integrity analysis across every corner and mode.
Predicts timing and IR-drop problems early, before full sign-off runs are needed.
Design-rule, electrical-rule and layout-versus-schematic checks, manufacturability analysis and mask data preparation for a clean foundry hand-off.
Pattern models flag yield-limiting layout shapes before they reach the fab.
Generative and agentic AI woven through the flow: assistants that answer questions, write scripts, and plan and run multi-step design tasks with an engineer in the loop.
Agents that launch, watch and debug tool runs — so engineers spend time on decisions, not babysitting jobs.
Most advanced process node supported
Faster verification closure with AI-ranked regressions
Perf-per-watt gain from hardware-aware model tuning
Years of technology delivery by the SFA group (since 2015)
Engage us for a single stage of your flow or the whole journey — every service is backed by our AI-accelerated engineering platform.
Our core focus: EDA software and flows with AI built in — RL floorplanning, ML-guided synthesis, predictive sign-off and LLM copilots that write and review RTL.
Domain-specific NPUs, tensor engines and SoCs architected around your models — optimised for TOPS/W, latency and cost.
UVM testbenches, formal proofs and AI-prioritised regressions that close coverage in weeks, not quarters.
Floorplan, place-and-route, clock-tree synthesis, STA, IR/EM and DRC/LVS sign-off at advanced FinFET and GAA nodes.
Train, compress and compile models for your silicon — quantisation, pruning, distillation and hardware-aware neural architecture search.
Pre-silicon validation on FPGA platforms plus firmware, drivers and SDKs so software is ready on day one.
Bring-up, characterisation, DFT/ATPG and ML-based yield analytics that find root causes faster.
Authentic components, obsolescence management and wafer-to-module logistics through a vetted global partner network.
Scroll to take one of our accelerator concepts apart. Every layer is engineered in-house or with trusted foundry and packaging partners.
A copper lid that pulls heat away from the compute die and protects the silicon beneath.
Tensor engines, on-chip SRAM and a RISC-V control cluster, placed with RL-driven floorplanning.
High-bandwidth memory stacks sit beside the die on a silicon interposer to feed models without stalls.
Fine-pitch routing fans thousands of signals out and delivers clean, stable power to every core.
The array of solder balls that connects the finished chip to the circuit board.
Great hardware needs models built for it. Our ML engineers co-design networks with the chip: we train on GPU clusters, then quantise, prune and compile so models hit accuracy targets at a fraction of the power.
Functional-safety-minded vision and sensor-fusion processors for driver assistance and autonomy.
High-throughput training and inference accelerators with HBM and chiplet scalability.
Ultra-low-power NPUs that run vision, voice and anomaly detection on battery budgets.
Baseband and AI-RAN silicon for massive MIMO and intelligent networks.
Imaging and wearable diagnostics chips with on-device AI for privacy and speed.
Secure-by-design, radiation-aware processors for mission-critical systems.
Real-time control and machine-vision SoCs for smart factories and autonomous robots.
Industry-led programmes built by practising engineers — hands-on labs on professional EDA flows, tape-out style projects and placement support.
Digital logic, CMOS, Verilog and the complete ASIC flow — the launchpad for a semiconductor career.
Build production-grade testbenches, coverage models and assertions used by leading design teams.
Floorplanning to sign-off: place-and-route, clock trees, timing closure and power integrity.
Design an NPU from scratch — dataflows, systolic arrays, quantisation and HW/SW co-design.
Apply ML and LLMs to EDA: RL floorplanning, predictive timing and RTL copilots.
Tailored programmes for engineering teams moving into AI silicon, verification or advanced nodes.
Their AI-driven verification flow closed coverage on our NPU weeks ahead of plan. The team felt like an extension of ours.
Chip design and model optimisation under one roof is rare. We got silicon and a model stack that simply worked together.
Graduates from SFA Academy join our team ready to contribute from the first week.
EDA is the category of software used to design chips — from writing and simulating the design, through synthesis and physical layout, to checking it is ready for manufacturing. Modern chips with billions of transistors could not be built without it. SFA Semicon adds AI throughout that flow so designs converge faster and with fewer iterations.
Our main focus is AI-powered EDA — tools, flows and services that speed up chip design itself. We also design AI-focused semiconductors from architecture to tape-out, optimise and train models for custom silicon, source components, and run training programmes for engineers.
Yes. We offer turnkey RTL-to-GDSII services and manage foundry, packaging and test partners — or we can plug into any single stage of your existing flow.
We use machine learning to rank verification tests, predict timing issues early, explore floorplans with reinforcement learning, and assist engineers with LLM-based RTL generation and review. That means fewer iterations and faster convergence.
Absolutely. Engagement models range from fixed-scope feasibility studies to dedicated engineering pods, designed for teams at every stage.
Every engagement is under NDA with strict access controls, isolated project environments and clear IP-ownership terms in the contract.
Send an enquiry through the contact form and choose “Academy admissions”. Our team will share batch dates, eligibility and fees.
Tell us about your workload, timeline and goals. We’ll come back within one business day with a plan.
A new accelerator, a verification crunch, model optimisation, component sourcing or academy admissions — we’d love to hear from you.
