category: Top Stories

category: Auto, Security & Edge AI

CPO Is Extending The Limits Of What’s Possible In AI Data Centers
By Ann Mutschler - 12 Mar, 2026 - Comments: 0

Key Takeaways I/O architecture must be co-designed with compute from day one. Partitioning SoCs into heterogeneous chiplets (compute, EIC, PIC, lasers) directly affects power delivery, floor-planning, interconnect topology, and system scalability. Successful CPO designs require architects to think in multi-physics terms, balancing electrical signaling, thermal stability, optical beha... » read more

AI Power on the Edge
By Brian Bailey - 12 Mar, 2026 - Comments: 0

Key takeaways Power and thermal become primary design considerations, not just optimizations. Hardware architectures need to be developed from the ground up. Hardware/software/model co-development is essential. Implementing AI on the edge is driven by a different set of metrics than training or even inference in the cloud. It makes power a first-class citizen, if not the mos... » read more

Scale Up, Scale Out Get a New Partner
By Bryon Moyer - 12 Mar, 2026 - Comments: 0

Key Takeaways: Three AI data center scaling strategies are scale-up, scale-out, and scale-across. Scale-up is within a rack; scale-out is between racks; scale-across is between data centers. Each of the three uses a different interconnect strategy to optimize either latency or jitter. As today’s data center workloads — especially for AI and HPC — outgrow the physical, ... » read more

Detecting Chemical Variability At Advanced Nodes
By Gregory Haley - 10 Mar, 2026 - Comments: 2

Key Takeaways Yield loss is increasingly driven by molecular variability in thin films, interfaces, and contamination rather than visible defects. Reliability issues often appear first as parametric drift or margin erosion under workload and thermal stress. Detection requires correlating molecular metrology, embedded electrical telemetry, and AI-driven wafer inspection. As s... » read more

Tool Matching Getting Tougher Across Test & Metrology
By Laura Peters - 10 Mar, 2026 - Comments: 0

Key Takeaways Engineers leverage both device-specific and tool-level data to identify a process "sweet spot." Tight, frequent tool-to-tool matching enables greater yield and fab flexibility. Machine learning helps capture the nuances of a tool's signature. Many people outside of the semiconductor industry wonder how humans can fabricate transistors with tens of nanometer sca... » read more

Digital Twins: The Cloud’s The Limit
By Ed Sperling - 10 Mar, 2026 - Comments: 1

Key Takeaways Digital twins are gaining traction as a way of testing different options at every step of the design-through-manufacturing flow. AI can be used to glue together disparate data types in multi-physics simulations. The promise of digital twins is huge, but multiple challenges need to be solved before it can live up to its potential. Digital twin technology is draw... » read more

Auto Security Accelerates With Standardization And Certified Silicon
By Ann Mutschler - 05 Mar, 2026 - Comments: 0

Key Takeaways The automotive sector is actively developing and delivering secure parts and features ranging from secure boot to encrypted data and in-network protections. The cost of a breach can involve everything from ransomware to liability and/or damage to a brand. New standards are being introduced to ensure security, and technology developers are integrating cybersecurity requi... » read more

Limiting AI/ML Tools To Ensure Physical AI Safety, Security
By Liz Allan - 05 Mar, 2026 - Comments: 0

Key Takeaways: AI-based tools can help monitor physical AI systems and LLMs, but human oversight is still needed to avoid false positives, bias, and other anomalies. For autonomous vehicles and robots, edge case scenarios and understanding human values are weak points, especially as moral and social values change over time. AI tools are growing and becoming increasingly helpful for c... » read more

New Automotive Architectures Are Shaking Up Processor And Memory Choices
By Ann Mutschler - 05 Mar, 2026 - Comments: 0

Key Takeaways Assisted and autonomous driving require more data from more sensors, and much faster processing of some of that data. The shift to software-defined vehicles and centralized intelligence makes it easier to identify where the most advanced processors and memories are required, and where older and less expensive technologies can be deployed. Technologies that were largely ... » read more

Tool And Methodology Changes Coming In Fab And Package Automation
By Gregory Haley - 04 Mar, 2026 - Comments: 0

Experts at the table: Semiconductor Engineering sat down to discuss what's changing in semiconductor fabs and packaging houses with Michael Lowman, senior product marketing manager for Data Analytics at Cohu; Aftkhar Aslam, CEO at yieldWerx, Woo Young Han, product marketing director at Onto Innovation; and Lihong Cao, senior director of engineering and technical marketing for ASE. What foll... » read more

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