Industrial Physical AI for semiconductor and advanced manufacturing. Connect physical models with process data to predict faster and explain decisions with evidence.
Semantic Graph moves beyond displaying detections by connecting manufacturing context, evidence, similar cases, and corrective-action history.
Review inspection images and decisions together with lot, material, and process-condition context.
Review ranked root-cause candidates together with the manufacturing records that support each conclusion.
Support the next engineering decision with standards, historical defects, action records, and similar cases.
These are representative interface modules for explaining the product workflow. Detailed capabilities and customer-specific scenarios are provided after an implementation inquiry.
Go beyond viewing inspection results. Connect evidence → root cause → next action in one engineering workflow.
Review inspection images and decisions together with lot, material, and process context in one evidence workspace.
Evaluate root-cause candidates together with linked manufacturing data, supporting evidence, and process context.
Support the engineer’s next decision using standards, historical defects, corrective actions, and similar cases as evidence.
We combine inspection intelligence with data security, explainable analysis, system integration, and a stepwise implementation pathway.
Connect defects, lots, materials, processes, equipment, and actions around real engineering workflows.
Review root-cause candidates and recommended actions together with supporting evidence and similar cases.
Operate manufacturing data and knowledge securely within enterprise or private environments.
Connect AOI, SWIR, camera, sensor, and quality data through a phased approach.
Reduce risk through inquiry review, technical assessment, data discussion, demos, and deployment planning.
Use anonymized public demos and conduct customer-specific assessment after NDA and security alignment.
The flagship goal is simple: complement and accelerate repeated thermal analysis with Physics AI for HBM, chiplets, and advanced packaging.
Inputs
Geometry · Material properties · Power · Boundary conditions · Thermal sensor data
Predictions
Temperature field · Hotspot · Thermal resistance · Reliability risk
Current stage: prototype / early product development. The product is positioned to complement and accelerate repeated CAE workflows, not to claim wholesale replacement of high-fidelity simulation.
As HBM moves toward higher stacks and higher power density, Thermal Engineering is becoming a product-level KPI for performance, reliability, and package design.
Background is based on publicly disclosed next-generation HBM packaging and thermal-management trends. Numerical examples are industry reference points, not performance claims for Semantic Graph's product.
NVIDIA GPU and CUDA remain core acceleration technologies, while PhysicsNeMo and Omniverse/OpenUSD are evaluated progressively. For field operations, we use an x86-based Physical AI Server as the primary platform; Jetson/TensorRT is applied selectively as an edge inference node after validating application, driver, and NVIDIA SDK compatibility.
Physics-informed and surrogate modeling for thermal and process behavior.
Model training, accelerated inference, and industrial AI workloads.
Geometry, simulation data, and manufacturing digital twin foundation.
Applied selectively for low-latency edge inference after validating ARM/JetPack application compatibility and NVIDIA SDK dependencies.
Semantic Graph is applying to NVIDIA Inception. NVIDIA Inception membership is not claimed on this website.
For enterprise operations, external services, maintainability, and software compatibility, we use x86_64 servers/workstations as the primary Physical AI operating platform. NVIDIA Jetson is reserved for validated low-latency edge inference close to equipment, with ARM/JetPack and SDK constraints reviewed in advance.
We design x86_64 Linux/Windows servers as the default operating platform for software compatibility, security, maintainability, and external-service integration.
Jetson is applied after validating ARM-based JetPack/Ubuntu application and driver compatibility, primarily for NVIDIA-SDK-centric low-latency inference close to cameras and sensors.
We connect industrial cameras, SWIR/thermal sensors, equipment I/O, storage, and networking to the x86 Physical AI Server and validated edge nodes.
Deployment principle. Jetson is not positioned as a universal server replacement. We first validate the customer's software stack and operating requirements, use x86 Physical AI Servers as the default platform, and add Jetson only where the NVIDIA ecosystem and edge workload are a clear fit. Hardware sourcing and integration do not imply NVIDIA-authorized distributor or reseller status.
Physics-Informed Thermal Digital Twin is being developed as our strategic flagship product, while completed SWIR, TILDA400, and Film demos demonstrate our manufacturing AI development and execution capability.
For HBM, chiplet, and advanced packaging, the Thermal Digital Twin combines geometry, material properties, power, boundary conditions, and thermal sensor data with physical laws and PINN/Physics AI surrogate models to predict temperature fields, hotspots, thermal resistance, and reliability risk. It is designed to complement and accelerate high-fidelity CAE.
A completed demo using SWIR (Short-Wave Infrared) imaging to detect low-contrast and internal anomaly signals that are difficult to identify with visible-light inspection, then connect them with defect, lot, equipment, and process context for quality decisions, root-cause analysis, and GraphRAG.
A manufacturing quality-intelligence demo using TILDA400 surface-defect data to connect defect-detection results with lot, process, cause, and corrective-action context, validating a workflow from inspection automation to explainable RCA and engineering action.
A completed AI vision inspection demo that detects and classifies scratches, foreign materials, stains, bubbles, wrinkles, and other surface defects in film, coating, and continuous web materials, then links defect location and type with lot and process context for quality root-cause analysis.
SWIR, TILDA400, and Film images are taken from actual Semantic Graph demo/prototype screens. Public demos use anonymized sample or synthetic data, and customer performance is validated separately with customer data and inspection criteria. The Thermal Digital Twin image is a reference concept based on a public NVIDIA PhysicsNeMo example, not a Semantic Graph product output.
Even when customer names remain confidential, transparent product and demo stages provide strong evidence of execution capability.
Productizing HBM thermal modeling through a Physics AI prototype pathway.
Proposal / PrototypeImplemented low-contrast/internal anomaly detection with an RCA/GraphRAG workflow.
Completed MVPValidated a quality-intelligence workflow from surface-defect detection to engineering action.
Completed DemoBuilt a film surface-inspection prototype with result drilldown.
PoC-readyPINNs (Physics-Informed Neural Networks) incorporate physical laws and boundary conditions into the learning process. We move Scientific ML beyond paper reproduction to build HBM Thermal Digital Twins and industrially validated prototypes.
The homepage summarizes the strategic themes. Detailed prior-art, novelty, and FTO considerations are moved to the Technology page.
Continuously calibrates an HBM thermal model by combining geometry, material, power, sensor data, and Physics AI residuals.
Connects inspection/process evidence with physics inconsistency to generate explainable root-cause paths.
Uses a Physics AI twin to validate AI-agent process actions before engineer-approved execution.
This is a preliminary roadmap and does not represent granted or filed patents. Novelty, inventive step, and FTO require separate professional review before filing.
We aim to enter through a measurable technical PoC, transition validated capabilities into repeatable product licenses, deploy on-premise, and expand across lines and plants.
Define the process problem, data, and measurable KPI.
Validate the core use case within a controlled scope.
Transition validated functions into repeatable product modules.
Deploy x86 Physical AI Servers as the primary operating platform and connect validated Jetson edge nodes only where low-latency field inference is required.
Scale Physical AI across processes, equipment, and lines.
Since 2022, Semantic Graph has built manufacturing AI capabilities across vision, Knowledge Graph, and GraphRAG, and is expanding its core product focus into Physics AI and semiconductor Thermal Digital Twins.
Semantic Graph delivers manufacturing AI through a stepwise pathway from public demos and customer-data PoCs to on-premise deployment and enterprise-system integration.
We translate Film AOI, low-contrast SWIR intelligence, manufacturing RCA, and industry-specific quality scenarios into customer-data PoCs.
Activities include KOIIA mid-market AX diagnostics and engagement around the Korea AI Software Industry Association (KOSA) Physical AI Alliance 2.0, supporting broader manufacturing Physical AI adoption and collaboration.
We have worked on industrial paper and patent RAG as well as manufacturing ERP and CRM integration PoCs that connect documents, business data, and operational events.
Our delivery ecosystem includes SWIR optical-inspection specialists, on-premise GPU infrastructure partners, NVIDIA Teaching Kits, and university and industry experts.
Choose the path that fits your goal: demo → focused PoC → deployment or collaboration.
Review public Film, SWIR, and TILDA400 demos to understand the product workflow and user experience.
Explore Demos 02Discuss inspection data, process context, KPIs, security, and deployment requirements.
Contact Us 03Explore joint opportunities across AI vision, optics, GPU infrastructure, and Knowledge AI.
Discuss Partnership 04Tailored Physical AI, PINN, thermal digital twin, and GraphRAG programs for organizational adoption and engineering teams.
View Education ProgramsShare these four items and we can quickly assess technical fit and the next step.
When useful, we follow up with a focused data review or a 30-minute technical discussion.