AI Framework Triples Bandwidth in Silicon Electro-optic Modulators

A 177% bandwidth increase marks a significant leap for optical hardware. Researchers have successfully paired Transformer-based deep learning with genetic algorithms to overhaul the design process for silicon electro-optic modulators. This breakthrough directly addresses the physical bottlenecks inherent in 100 Gbps+ data transmission, offering a scalable alternative to manual engineering.

Optimising Silicon Electro-optic Modulators

Data centres demand speed. However, parasitic effects in driver circuits severely cap the performance of silicon-based components. Traditional design relies on electromagnetic simulations. These are sluggish. Engineers must manually tune parameters, often missing the optimal configuration. This inefficiency creates a barrier to scaling. The study introduces a framework that automates geometry discovery. It moves beyond empirical guesswork.

The Transformer Mechanism

The core innovation is a surrogate model. The team constructed a high-fidelity dataset of 10,000 inductor samples using Advanced Design System (ADS) simulations. A Transformer-based neural network ingested this data. It learned to predict key performance metrics with an average error below 5%. This accuracy exceeds standard physical models.

Once trained, this model accelerates the design loop. It feeds a multi-objective genetic algorithm. The algorithm explores the design space rapidly. It identifies inductor geometries that balance conflicting requirements. It seeks the Pareto frontier—the set of optimal trade-offs—without the computational drag of full-wave simulations.

Measured Impact and Utility

The results are stark. Bandwidth surged from 26 GHz to 72 GHz. In system-level simulations under 100 Gbps NRZ modulation, signal quality improved significantly. The eye opening increased by 50%. The bit error rate dropped by one order of magnitude.

These metrics suggest a viable path for next-generation interconnects. The "data-driven modelling + intelligent optimisation" approach provides a scalable template. While applied here to inductors, the method could streamline the creation of other micro-photonic devices. It reduces development cycles from weeks to hours. Reliability increases. Cost decreases. The bottleneck of physical simulation is effectively broken.