AI Optimisation Triples Bandwidth in Silicon Mach-Zehnder Modulator Designs

The Bottleneck: Silicon Mach-Zehnder Modulator Limitations

Researchers have utilised artificial intelligence to break a critical deadlock in optical communications. A new study reports a massive 177% bandwidth improvement in a silicon Mach-Zehnder modulator, moving from 26 GHz to 72 GHz. Data centres currently demand 100 Gbps+ optical interconnects to handle exponential traffic growth. However, silicon-based modulators frequently hit a wall. Parasitic effects in driver circuits restrict bandwidth, creating a bottleneck that slows down the entire network.

Traditional engineering fails to solve this efficiently. Designers rely on electromagnetic simulations. These are slow. They require iterative, empirical tuning. The process consumes time and often yields suboptimal results. Engineers are effectively guessing, simulating, and guessing again. This manual approach cannot keep pace with the physical demands of modern high-speed data transmission.

The Solution: Transformer-Based Intelligent Design

The research team proposes a radical shift from empirical tuning to algorithmic prediction. They constructed a design framework that integrates a Transformer-based deep learning predictor with a multi-objective genetic algorithm. This is not standard automation. It is a learning system.

To train the system, the team generated a high-fidelity dataset comprising over 10,000 inductor samples using Advanced Design System (ADS) simulations. This data taught the neural network the physics of the component. The resulting Transformer model predicts key performance metrics with an average error below 5%. It outperforms both traditional physical models and other neural architectures in accuracy.

Mechanism: Rapid Exploration of Design Space

The neural network acts as a surrogate for slow physical simulations. When the genetic algorithm seeks the best inductor geometry, it queries the Transformer model. Answers are instant. This allows the system to explore the design space rapidly, identifying Pareto-optimal geometries that a human engineer might miss.

Instead of days of computation, the framework iterates through thousands of possibilities in a fraction of the time. It balances conflicting objectives—size, bandwidth, and signal integrity—without human intervention. The AI filters out poor designs before they ever reach the simulation stage.

Impact: Validating the 100 Gbps Benchmark

System-level simulations confirm the efficacy of this approach. Under 100 Gbps NRZ modulation, the optimised designs demonstrated a 50% larger eye opening compared to standard baselines. Furthermore, the bit error rate dropped by one order of magnitude. These metrics suggest that the optimised modulator can handle high-speed traffic with superior reliability.

The implications extend beyond a single component. This "data-driven modelling + intelligent optimisation" framework offers a scalable template. While this study focused on inductors, the methodology could theoretically apply to other micro and nano photonic devices. The industry may soon replace manual trial-and-error with high-precision algorithmic generation.