EE Times Current

In this episode, we’ll explore Siemens EDA’s innovative approach to AI and dive into Calibre-specific topics. We’ll discuss when to use Calibre, when it might not be the best fit, and how to leverage it to maximize productivity and designer effectiveness—all without compromising on quality.AI is everywhere these days, from space exploration to dating apps, and Siemens has been a pioneer in this space, investing in AI long before it became a trend. So, let’s dive in and explore how Siemens EDA is shaping the future of design with AI.

Today we’re talking about something that’s top-of-mind for a lot of you: closing the reliability gaps in increasingly complex analog and mixed-signal IC designs—and doing it earlier, faster, and more systematically.As designs become more heterogeneous and integration of IP blocks more intricate, traditional simulation and ERC tools often aren’t enough. They’re reactive by nature, catching issues too late in the flow—when rework is costly, and design intent is harder to trace.That’s why “shift-left” verification has become more than just a buzzword. It’s a strategic necessity. And today’s conversation is all about one of the tools helping to make that shift actionable: Siemens’ Insight Analyzer.

Professor Gordon Cheng builds humanoid robots that can feel their environment using artificial skin. In this episode of Brains and Machines, he talks to Dr. Sunny Bains of University College London about how the skin was designed, how it improves safety and why neuromorphic engineering will be important for machine autonomy. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Professor Ralph Etienne-Cummings of Johns Hopkins University.

The semiconductor industry is undergoing a shift with the rapid adoption of multi-die design, driven by the promise of improved power, performance, and area (PPA). But with innovation comes complexity, and one of the biggest challenges is ensuring silicon reliability and health through effective multi-die testing.In this episode, we dive deep into the world of multi-die design for test: what it means, how it differs from traditional monolithic design testing, and why it’s critical for the future of semiconductor manufacturing. Learn how testing spans from individual dies to multiple dies to die-to-die links, and why silicon data is essential for maintaining multi-die health during both manufacturing and in-field operations. We will explore the future of multi-die design for test and discuss Silicon Lifecycle Management (SLM) strategies that designers can implement today to stay ahead.

In this episode of Brains and Machines, Dr. Terry Sejnowski talks to Dr. Sunny Bains of the University College London about how information flows both ways between neuroscience and engineered intelligence, proposes a new way of looking at memory and considers the Hopfield-Hinton Nobel Prize.

Dr. Tony Lewis, CTO of BrainChip, and four other key scientists talk to Dr. Sunny Bains of University College London. They discuss their business strategy, their temporal event-based neural network (TENN) and the next iteration of the Akida chip. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Prof. Ralph Etienne-Cummings of Johns Hopkins University.