The future of semiconductors: Engineering in the convergence era
Reflections from inside an industry undergoing its biggest transformation in decades
The semiconductor industry is entering a convergence era where silicon, software, physics, packaging, security, AI, and power constraints all intertwine. Device scaling still matters but architecture, integration, verification, and automation will define the industry’s trajectory. Organizations that embrace this cross-domain, lifecycle-oriented mindset will define the next decade.
Moore’s Law didn’t “die.” What changed is the economic engine behind it. Transistors still shrink, but the cost-per-transistor advantage that powered decades of predictable scaling is no longer guaranteed. Today, real breakthroughs come from system-level engineering — from the way we assemble, integrate, and optimize entire platforms.
Chip-level orthogonalization isn’t gone — but it’s no longer airtight. At the system level, however, orthogonalization has collapsed. This shift reflects a broader trend across engineering: electrical, mechanical, physical, and software domains are converging into one integrated system problem. Silicon is simply the first place this convergence is unavoidable.
As boundaries blur, verification must reason across domains that used to be separate. This is why digital twins are becoming essential — not the buzzword kind, but the real kind. No single engine can express system-level truth. Only when these engines converge do we get the fidelity required to understand modern systems.
To keep up, products have become software-defined. They update firmware, drivers, orchestration layers, and even on-device AI models continuously after deployment. This flexibility is essential! Verification becomes continuous, not a phase. And digital twins become the bridge that connects design-time assumptions with field-time behavior.
General-purpose scaling has plateaued just as workloads have become more specialized. AI, robotics, graphics, vision, wireless, and real-time control all demand custom or semi-custom architectures. Domain-specific architectures (DSAs) are no longer niche — they’re essential. Hardware and software now co-evolve, and verification must track that moving boundary.
AI already enhances individual verification tools. But the next leap — and perhaps the most disruptive — is agentic AI orchestrating entire verification workflows. This is not “AI as a feature.” This is AI as the conductor of the entire verification process. In other words: Engineers provide intent, agentic AI handles iteration depth.
Given the complexity, dynamism, and multidisciplinary nature of modern systems, agentic AI will become foundational — not optional — to verification productivity. Unified environments such as Questa One are deliberately evolving to provide the substrate agentic AI will orchestrate across.