Deep Learning Framework Claims 177% Bandwidth Boost for Silicon Mach-Zehnder Modulator

A new design framework purports to increase the bandwidth of optical components by nearly 180 per cent, theoretically enabling 100 Gbps data transmission rates. For decades, engineers have struggled to manually tune the Silicon Mach-Zehnder Modulator, a device notorious for hitting performance ceilings due to parasitic electrical effects in its driver circuits.

The core challenge in silicon photonics has always been the trade-off between accuracy and time. To mitigate parasitic capacitance and resistance, designers typically introduce peaking inductors. However, finding the optimal geometry for these inductors is arduous. The standard approach involves iterative electromagnetic (EM) simulations, where each potential design is rigorously tested against Maxwell’s equations. This process is accurate but computationally expensive, often taking days to yield a suboptimal result. The sheer volume of variables makes manual tuning inefficient.

Contrasting EM Solvers with Transformer Logic

The study introduces a technical divergence between established protocols and this new data-driven approach. Traditional methodology depends on direct electromagnetic field solvers. These act as a bottleneck, restricting the number of geometries an engineer can test because each data point requires a full physics simulation. Conversely, the proposed framework utilises a Transformer-based neural network. By treating inductor geometry parameters as sequential data—akin to sentence structure in natural language processing—the model predicts bandwidth and inductance without solving the underlying physics equations each time. While the traditional method offers certainty at the cost of speed, the Transformer model offers rapid statistical inference, allowing the system to scan thousands of possibilities in a fraction of the time.

Using this surrogate model, the researchers generated a dataset of over 10,000 inductor samples using Advanced Design System (ADS) simulations. They report that their neural network predicts performance metrics with an average error below 5 per cent. This speed allowed them to employ a multi-objective genetic algorithm to identify Pareto-optimal geometries. The study measured a bandwidth increase from 26 GHz to 72 GHz—a 177 per cent improvement.

Optimising the Silicon Mach-Zehnder Modulator

System-level simulations under 100 Gbps NRZ modulation suggest a 50 per cent larger eye opening and a significant reduction in bit error rate. These metrics imply that the new design could handle next-generation traffic demands more robustly than current iterations. The framework essentially automates the 'trial and error' phase of engineering, replacing human intuition with algorithmic sorting.

However, a degree of scepticism is warranted regarding the translation from code to silicon. The framework relies entirely on a 'surrogate model' trained on simulation data, not physical measurements. If the initial ADS simulations fail to account for specific fabrication variances or material impurities found in a real foundry, the AI will merely optimise for a flawed reality. While the theoretical gains are substantial, the paper presents a simulation of a simulation. Physical validation of the manufactured chips will be required to confirm if the 72 GHz bandwidth holds up outside the digital environment.