SemiAI

What SemiAI does

SemiAI revolutionizes semiconductor manufacturing with an Agentic AI platform called SMILE. This platform forecasts process drift early, accelerating engineers' decision cycles and moving beyond reactive APC to proactive control driven by prediction and causal reasoning. SMILE offers modular solutions for analysis, prediction, and real-time control, built on a human-in-the-loop protocol. It addresses challenges like fragmented analysis tools, manual sampling, and limited feedback in semiconductor manufacturing by providing semiconductor-native visualizations, automated sampling optimization, and multi-stage correction models. The platform is designed to be deployed to meet fab security and compliance, with options for both cloud and on-premise deployments. SemiAI's approach is domain-first, using embeddings and Trace designed around the physics and workflow of a real fab, and employs agents that act under human-in-the-loop supervision. The systems are validated using both Virtual Fab synthetic data and real fab data to ensure production safety.

Products

• SMILEA modular AI platform spanning analysis, prediction, and real-time control, adopted at the maturity your process is ready for. It unifies engineer chart analysis and reporting in one workflow to speed up decisions.
• SMILE LiteUnifies analysis, sampling, and modeling into one workbench to accelerate HVM decisions. It provides real-time detection with multimodal analysis, compressing chart-based investigation from days into minutes.
• SMILE ProCompresses analysis time with real-time detection and multimodal reasoning. It redesigns APC with predictive AI, moving beyond reactive control to proactive control driven by prediction and causal reasoning.
• SMILE Pro+Accelerates the engineer's analysis workflow end-to-end with two autonomous modes. It features natural-language commands, context-aware responses, visible AI reasoning, and an automated RCA pipeline.
• PRISMA real-time prediction engine for process drift. It predicts process hotspots in real time and proposes corrections for the next lot, catching drift before APC reacts.
• INFERA causal reasoning engine for root cause analysis. It narrows root causes from dozens of candidates using causal reasoning over a domain knowledge base, with engineers validating each step.
• Virtual FabA synthetic data infrastructure for AI training. It solves data scarcity and missing edge-case coverage with domain knowledge-based synthetic generation, generating synthetic wafer data grounded in process, equipment, and defect rules.
• Semi-BrainA multimodal inference engine reasoning over charts, process context, and a domain knowledge base. It provides multimodal first-pass interpretation and conversational follow-ups.
• Business Intelligence SuiteA BI suite built on semiconductor-native visualizations and chained drill-downs, offering a semiconductor-native chart library and bookmarkable dashboards.
• Sampling OptimizerAutomates the trade-off between metrology throughput and model fidelity. It generates die-grid layout cases, places overlay keys, and previews per-model trends.
• Defect Model SimulatorTrains, manages, and simulates overlay correction recipes in one workspace. It includes physically meaningful multi-stage model training and recipe library management.
• Semi-VisionAn SEM measurement alignment engine. It provides cross-tool measurement alignment, AI defect inspection and classification, and throughput optimization.
• EPE BudgetA yield-linked EPE budget prioritization engine. It offers gauge selection, component-entry extraction, 3D-aware proxies inferred from 2D metrology, and multi-target linkage.

Key capabilities

• Agentic AI platform
• Human-in-the-loop protocol
• Real-time detection and multimodal analysis
• Predictive AI for proactive control
• Semiconductor-native visualizations
• Automated sampling optimization
• Multi-stage overlay correction models
• Real-time prediction engine for process drift (PRISM)
• Causal reasoning engine for root cause analysis (INFER)
• Synthetic data infrastructure for AI training (Virtual Fab)
• Multimodal inference engine (Semi-Brain)
• SEM measurement alignment engine (Semi-Vision)
• Yield-linked EPE budget prioritization engine (EPE Budget)
• Conversational AI assistant (SemiAI Assistant)
• Automated RCA pipeline
• Fact-First Reasoning with Causal RAG
• Cloud and On-Premise deployment options
• Privacy-preserving data pipeline
• SSO / IAM integration
• Automatic and signed model update bundles

Use cases

• Forecasting process drift early
• Accelerating engineers' decision cycles
• Proactive control in semiconductor manufacturing
• Real-time detection of process issues
• Compressing chart-based investigation time
• Unifying engineer chart analysis and reporting
• Pinpointing root causes of defects
• Automating the trade-off between metrology throughput and model fidelity
• Predicting wafer-level control of overlay hotspots in photolithography
• Predicting issue wafers via Virtual Metrology
• Feed-forward Lot N -> N+1 control
• Wafer-level control without rework
• Automated issue resolution
• Generating synthetic data for AI training
• Covering rare defect signatures
• Interpreting charts and process context with AI
• Prioritizing EPE budget components
• Training, managing, and simulating overlay correction recipes
• Cross-tool measurement alignment
• AI defect inspection and classification
• Throughput optimization
• Accelerating HVM decisions
• Automated report generation

AI approach

SemiAI develops an Agentic AI platform, SMILE, specifically for semiconductor manufacturing. It uses proprietary AI models and synthetic data generation (Virtual Fab) to predict process drift, perform causal root cause analysis, and automate workflows. The approach is 'domain-first AI', designing embeddings and traces around the physics and workflow of a real fab, rather than using off-the-shelf LLM/ML. It incorporates human-in-the-loop protocols for validation and decision-making.

Tech named: Agentic AI, ML/AI-based yield improvement systems, LLM, embeddings, Trace, Virtual Fab synthetic data, XGBoost, Causal RAG, ML engineer

Industries served

• Semiconductor manufacturing

What it says sets it apart

• Domain-first AI: Embeddings and Trace designed around the physics and workflow of a real fab, not off-the-shelf LLM/ML.
• Agents that act: Agents carry through to action under human-in-the-loop supervision, not just observation and recommendation.
• Validated in the fab: Systems are production-safe on day one through cross-validation of Virtual Fab synthetic data and real fab data.
• Modular AI platform: Solutions can run standalone or together, adapting to process maturity.
• End-to-end workflow: Connects filtering, charting, AI interpretation, recommended analysis, and report generation without switching tools.
• Fact-First Reasoning: Causal RAG built in-house on Virtual Fab surfaces defect clues first and only allows hypotheses grounded in data, preempting hallucination.

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.