Semiconductor R&D and manufacturing use computation across device and process simulation, mask/layout analysis, computational imaging, metrology, defect inspection, yield analytics and equipment intelligence. These workloads are diverse, but they share a need for controlled software environments, large protected datasets and strong intellectual-property boundaries.
Hyperion offers scheduled CPU facilities, high-memory multi-GPU development and two-node AI workgroups that can remain inside the organisation.
Simulation and computational engineering
X3140 AxiLattice provides three Slurm-managed nodes, 384 CPU cores and 3.4TB aggregate memory for shared process/device, numerical and workflow workloads. Benchmark named codes and model sizes; memory bandwidth, licence availability and storage metadata can limit throughput before core count. Preserve input decks, libraries, environment and numerical regression evidence.
Accelerated modelling and computational imaging
X1460 AxiForge Max supplies four 96GB GPUs and 1.15TB host memory for supported AI, inverse, reconstruction and generative workloads. Profile data preparation, transfers and collectives rather than assuming peak GPU capability becomes application performance. Keep trained models linked to process, tool, acquisition and dataset versions.
Defect inspection and yield intelligence
High-resolution inspection can require tiled inference, large host memory and controlled multi-GPU throughput. X2260 AxiCrucible separates model development/validation from production services across two nodes. Evaluate by product, layer, tool, defect class and time; prevent split leakage and preserve human disposition.
Secure multi-user platform
Integrate central identity, project/tenant isolation, signed containers, quota/accounting, encrypted protected storage and management-network separation. Restrict unreviewed egress and maintain auditable model/data promotion. A private RAG service can retrieve approved process and equipment knowledge only when document permissions are applied before retrieval.
Reproducibility and change control
Record code commit, container digest, driver/runtime, parameter deck, source data and hardware allocation. CI should include numerical, performance and data-contract tests. Revalidate material changes to model, feature, calibration, dependency or equipment interface before operational use.
Automation, AI and machine engineering for Semiconductor
Connect sector workloads to the production equipment
Combine precision motion, equipment integration, traceability, anomaly detection and controlled software delivery for complex production and laboratory equipment.
Relevant services