Cloud Services in Seoul

Manufacturing / Semiconductor and Electronics

A Korean semiconductor company (top-3 global memory manufacturer, KRW 65T annual revenue, 55,000 employees — manufacturing DRAM, NAND flash, and advanced logic semiconductors across 8 fabrication facilities in Korea, China, and the US) needed cloud infrastructure to support chip design, manufacturing analytics, and enterprise operations while maintaining K-ISMS-P compliance and managing the extreme compute requirements of semiconductor engineering. (1) EDA compute crisis: chip design required Electronic Design Automation tools that consumed massive compute resources — a single advanced logic design verification run required 50,000+ CPU cores for 72 hours. On-premises compute clusters (20,000 cores) were oversubscribed, with engineering teams waiting 2-3 weeks for compute allocation. Each week of design delay translated to approximately KRW 200B in lost market opportunity (first-mover advantage in semiconductor markets is measured in weeks). (2) Fab data explosion: each fabrication facility generated 15TB of data per day — equipment sensor data, process measurements, wafer inspection images, and test results. Total fab data volume was growing at 40% annually. On-premises data lakes were reaching capacity limits, and expanding on-premises storage required 6-month procurement cycles and KRW 50B capital expenditure. (3) Yield analytics required cloud: improving semiconductor yield by even 0.1% was worth KRW 100B+ annually at the company's production scale. Advanced yield analytics required ML models processing the full fab data corpus — but on-premises compute could not support the model training at the required scale. Models that took 2 weeks to train on-premises needed to be retrained daily to keep up with process changes. (4) K-ISMS-P for semiconductor IP: semiconductor design data and manufacturing process parameters represented the company's most valuable intellectual property. K-ISMS-P certification was required, but beyond regulatory compliance, the company needed cloud security that protected against nation-state-level IP theft. Semiconductor trade secrets on cloud required defence-in-depth security exceeding standard K-ISMS-P requirements. (5) Export control: semiconductor technology was subject to Korean and international export controls — especially advanced process nodes under 14nm. Cloud infrastructure needed to enforce export control boundaries — preventing controlled technology data from being processed on cloud infrastructure in restricted jurisdictions.

We designed and implemented a semiconductor cloud platform over 16 weeks. (1) EDA cloud: high-performance compute for chip design — dedicated AWS instances (compute-optimised and memory-optimised) in the ap-northeast-2 region with placement groups for inter-instance communication performance. Burst scaling: from 5,000 baseline cores to 80,000+ cores for peak design verification runs — eliminating the 2-3 week compute queue. Licensing: EDA license orchestration — managing expensive EDA software licenses across cloud compute to maximise utilisation (licenses costing millions of dollars per year needed to be used efficiently). Secure design vault: semiconductor design data encrypted at rest (ARIA-256) with access limited to authenticated design engineers — data never leaving Korean cloud region. (2) Fab data lake: cloud-native data lake on AWS S3 — ingesting 15TB per day from each fab through dedicated Direct Connect links. Data processing: Apache Spark on EMR for batch processing, real-time streaming through Kinesis for equipment alerts and SPC signals. Storage tiering: hot data (recent 90 days) on S3 Standard, warm data (90 days to 1 year) on S3 IA, cold data (beyond 1 year) on S3 Glacier — reducing storage costs by 60% compared to single-tier on-premises storage. Data governance: automated classification of fab data by sensitivity level — process recipes and equipment parameters as highest sensitivity, aggregate production metrics as standard sensitivity. (3) Yield analytics platform: ML training infrastructure — GPU clusters (NVIDIA A100) for training yield prediction models. Training cycle: from 2 weeks (on-premises) to 8 hours (cloud GPU cluster) — enabling daily model retraining with the latest fab data. Inference: real-time yield prediction at the wafer level — models running on dedicated inference endpoints providing predictions to fab engineers within seconds of wafer measurement. Defect detection: deep learning models analysing wafer inspection images — detecting defect patterns that indicated systematic process issues. The models identified 3 novel defect mechanisms in the first 6 months that human inspection had missed. (4) Security architecture: K-ISMS-P compliance with enhanced IP protection — network isolation (dedicated VPCs with no internet access for semiconductor IP workloads), encryption (ARIA-256 for data at rest, TLS 1.3 for transit, with customer-managed KMS keys), access control (MFA with hardware tokens for design data access, role-based permissions with least-privilege principle), audit logging (comprehensive access logging with automated anomaly detection — unusual access patterns flagged within minutes), and threat monitoring (24/7 SOC monitoring of cloud infrastructure — enhanced monitoring for semiconductor IP workloads with lower alert thresholds). (5) Export control: geographic controls (semiconductor technology data restricted to approved cloud regions — Korean and US regions only, with technical controls preventing data replication to restricted jurisdictions), access controls (user location verification for export-controlled data access — VPN with geographic IP validation), data flow monitoring (automated monitoring of data flows between cloud regions — flagging any transfer of controlled data to unapproved destinations), and audit (comprehensive export control audit trail — documenting all access to controlled technology data for regulatory examination).

Design productivity: compute queue from 2-3 weeks to under 2 hours — design verification runs starting within hours rather than weeks. Engineering productivity increased by 45% — design teams completing more verification cycles, finding more bugs earlier, and achieving faster time-to-tapeout. Time-to-market: 6-week reduction in average chip development cycle — worth approximately KRW 1.2T in first-mover advantage at the company's scale. Fab data: cloud data lake eliminated the KRW 50B storage expansion capital expenditure — while providing 40% storage cost reduction through intelligent tiering. Data processing: batch analytics from 12 hours (on-premises) to 2 hours (cloud) — enabling shift-by-shift rather than daily analytics. Yield improvement: daily model retraining enabled yield prediction models that tracked process changes in real-time. Yield improvement attributed to enhanced analytics: 0.3% in the first year — worth KRW 195B at the company's production scale. Novel defect detection: 3 previously undetected defect mechanisms identified — early intervention prevented estimated KRW 45B in wafer losses. Security: K-ISMS-P certification achieved for the cloud platform — with enhanced IP protection measures exceeding standard requirements. Zero security incidents in 18 months. Export control: zero export control violations — automated controls preventing unauthorised data movement. Annual value: KRW 1,200B (time-to-market — design cycle reduction accelerating chip availability), KRW 195B (yield improvement — 0.3% yield increase from enhanced analytics), KRW 120B (engineering productivity — design team efficiency improvement reducing R&D costs), KRW 85B (infrastructure savings — eliminated storage capex, reduced compute costs through cloud elasticity), KRW 45B (defect prevention — early detection of novel defect mechanisms) = KRW 1,645B (approximately USD 1.26B). Development cost: KRW 9.5B (approximately USD 7.3M). Payback: under 3 days.