HeyJaredSemiconductor Engineering
Semiconductor Engineering is a specialized industry publication focused on the technical, manufacturing, and business challenges of designing and producing advanced semiconductor devices.
- Indexed articles, last 90 days
- 83
- Latest publication
- Sep 24, 2026
- Audience
- Top 1M sites
- Earliest in this view
- Jul 3, 2026
Latest articles
A Network-on-Chip (NoC) For Multi-Die Devices (opens the original)
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The semiconductor industry is rapidly transitioning from monolithic SoCs to chiplet-based architectures. At the same time, AI workloads are evolving beyond cloud inference into physical AI: systems that perceive, reason, and act in the real world. Autonomous vehicles, industrial robots, humanoid robots, drones, and intelligent manufacturing systems all require continuous, high-volume data flow between heterogeneous compute engines. However, these two trends expose a weakness in today’s die-to-di
Managing 3D-IC Design And IP (opens the original)
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By Keith Felton and Todd Burkholder The trend towards multi-chiplet heterogeneous integration is here to stay. It promises faster innovation, more powerful and specialized devices, and greater design flexibility. However, realizing its potential hinges on our ability to manage the complexity that comes with it. However, with this newfound modularity comes a significant challenge: managing the vast and intricate web of data that defines these systems. This is where robust data management and meti
When AI Agents Cross Chip Design Silos (opens the original)
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Key Takeaways: Chip design is moving toward specialized AI agents working together. Orchestration, integration, and guardrails are becoming critical. Human engineers still play a central role in guiding and validating designs and processes. AI agents are becoming smarter, more capable, and increasingly optimized for specific tasks, creating new opportunities to use smaller language models instead of relying on large language models for every task. This shift has broad implications for the semico
EDA’s Future Is Evidence-Driven Automation (opens the original)
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Key Takeaways: AI’s biggest EDA opportunity is workflow-level orchestration that coordinates tools, people, constraints, and domain knowledge across traditionally fragmented design stages, rather than merely improving individual tools. Trust requires evidence and semantic continuity. AI-generated recommendations or assertions must preserve design intent and remain independently verifiable through deterministic verification, formal proof, reproducibility, and audit trails. The emerging EDA model
AI-Driven Device Modeling For Next Generation Quantum Applications (opens the original)
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Key takeaways: Accelerate quantum system design by rapidly developing accurate device models for circuit-level simulation. Reduce manual model-tuning effort with Keysight ML Optimizer, enabling efficient, derivative-free extraction of advanced compact model parameters. Extend compact models to cryogenic temperatures using deep learning neural networks (NN) to learn unmodeled residual behavior from 4 K or below measurement data. Quantum computing is advancing rapidly from isolated research device
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Audience
Top 1M sites
Measured Aug 1, 2026
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