The scaling model that once drove semiconductor progress has reached its limits. For decades, the industry delivered on its promise, doubling the number of transistors on a chip every two years and enabling exponential leaps in computing. However, physical limitations, escalating costs, and design complexity have disrupted this rhythm. Erik Hosler, a semiconductor strategist and expert in patterning and photonics integration, points to a new solution that is less about node shrinkage and more about combining strengths across disciplines.
It will take a collective strategy spanning materials science, system architecture, software, photonics, and Microelectromechanical Systems (MEMS) to preserve the spirit of Moore’s Law. Convergence, rather than the continuation of traditional scaling, will define the next generation of computing progress.
A Law Stretched to Its Limits
By the late 2010s, Moore’s Law had already begun to falter. While process nodes reached 5nm and below, the complexity of fabrication surged. More masks, more steps, and more uncertainty entered the equation. The effort required to gain marginal speed or power advantages increased dramatically.
And yet, computing demands only grew. AI workloads, immersive media, real-time analytics, and edge computing have placed new pressures on chips. These needs could not be met by transistor density alone. They required smarter architecture and new physical tools. The idea that logic shrinking alone would sustain performance was no longer tenable.
Enter Convergence: A Multidisciplinary Response
Rather than fighting the limits of lithography, many companies are now embracing a broader response, converging diverse technologies to deliver system-level improvements. The concept is simple, combining the best tools for each job into a cohesive platform. In practice, this means integrating electrical, mechanical, and optical systems into a new breed of chip architecture.
Accelerators for machine learning, photonic interconnects for high-speed data movement, and MEMS for environmental awareness and control each fill a niche. Together, they build systems that are not only powerful but also more adaptable and efficient. This convergence transforms chip design from a linear path into a layered ecosystem.
A Quote That Frames the Future
Erik Hosler notes, “It’s going to involve innovation across multiple different sectors.” This statement captures what many in the industry now believe: the next era of chip progress depends on collaboration. Success will not emerge from deeper etches or new transistor geometries alone. It will come from the ability to combine insights and breakthroughs across disciplines.
Materials engineers must align with optical physicists, and system architects must coordinate with machine learning scientists. The edges between sectors are dissolving, and in their place, shared opportunity is forming.
Photonics: From Theory to Foundry
Photonics has long held promise in research circles, but it is finally making its way into mainstream semiconductor applications. Silicon photonics allows light-based data transfer within and between chips, bypassing the limitations of traditional copper wires.
By reducing latency and energy by a bit, photonic interconnects solve one of the biggest bottlenecks in modern computing. They are particularly effective in AI workloads and high-bandwidth data centers where electrical signals struggle to keep up. Incorporating photonics is no longer an experiment. It is a strategy for staying ahead.
MEMS: Adding Intelligence at the Edge
MEMS technologies are another pillar of convergence. These microscopic machines can sense, actuate, and respond to the physical world within a chip-sized footprint. They are already found in devices like accelerometers and gyroscopes, but their value in advanced chips is rising fast.
MEMS brings contextual intelligence to silicon. They enable chips to react to motion, pressure, vibration, or light without needing external systems. For wearables, automotive sensors, and edge computing, this capability makes a major difference. Their compatibility with semiconductor manufacturing processes also makes it easier to scale.
Packaging and Architecture Join the Mix
Beyond photonics and MEMS, advances in packaging are also critical. Chiplets, 2.5D interposers, and 3D stacked dies allow designers to mix different technologies into one unified system. Logic, memory, I/O, and sensing can all be integrated on different process nodes and stitched together with advanced interconnects.
Architecturally, designers are shifting toward domain-specific compute. GPUs for graphics, TPUs for AI, and DSPs for signal processing, each optimized for its workload. The result is a heterogeneous computing model that thrives on convergence, not just performance per core. In this way, even traditional chip companies are becoming system integrators.
Collaboration Is the New Competitive Edge
None of these advancements occurs in isolation. They require tight coordination between research labs, design teams, manufacturing partners, and even policymakers. The complexity of convergence demands trust, transparency, and shared goals.
We now see more consortia, academic partnerships, and open standards of effort than ever before. Companies like Intel, TSMC, IBM, and startups recognize that success will depend not just on intellectual property but also on shared infrastructure.
Measuring Progress Differently
With so many moving parts, how do we know if convergence is working? Traditional metrics like transistor count or clock speed are no longer enough. New benchmarks are emerging for system throughput, energy efficiency, context awareness, and user experience.
What matters is not how small a transistor is but how well a chip performs in its real-world task. Can it run AI inference in real time on the edge? Can it maintain privacy while managing local health data? Can it interface cleanly with multiple sensors and outputs? Convergence supports these goals in ways that raw scaling no longer can.
Convergence Is Not a Compromise, It’s a Strategy
Moore’s Law may not follow the same curve it once did, but that does not mean progress has stalled. The opposite is true: we are entering a period of rich, multidimensional growth. By blending the strengths of diverse sectors: mechanical, optical, electrical, and algorithmic, we are creating technologies that far exceed what traditional chips could achieve alone.
It is a new direction for computing. It is not bound by one material, one architecture, or one roadmap. It is guided by a willingness to integrate, iterate, and improve. And that is how Moore’s Law will be saved, not by shrinking further but by thinking bigger.
