AI Framework Triples Bandwidth in Silicon Mach-Zehnder Modulator Design

Accelerating the Silicon Mach-Zehnder Modulator

Engineers have successfully integrated deep learning with genetic algorithms to overcome severe physical limitations in optical hardware. This approach specifically targets the Silicon Mach-Zehnder Modulator, a component essential for converting electrical signals into light but often throttled by parasitic effects in driver circuits. The study demonstrates a method to push bandwidth from 26 GHz to 72 GHz. Immediate utility lies in stabilising 100 Gbps optical interconnects, which are currently demanded by exponential data traffic growth.

These results were observed under controlled laboratory conditions, so real-world performance may differ.

The Problem: Parasitic Throttling

Data centres require speed. However, physical constraints in silicon photonics create a bottleneck. As data rates climb toward 100 Gbps, parasitic capacitance and resistance degrade signal quality. The traditional design process exacerbates this issue. Engineers typically rely on electromagnetic simulations and empirical tuning. This is slow. It is computationally expensive. It frequently results in suboptimal inductor geometries that cannot support higher frequencies. The hardware lags behind the data demand.

The Solution: Transformer-Based Prediction

To break this cycle, the research team constructed a high-fidelity dataset comprising over 10,000 inductor samples using Advanced Design System (ADS) simulations. They did not rely on standard physical models. Instead, they trained a Transformer-based neural network. This deep learning architecture excels at handling sequential data and complex dependencies.

The network learned to predict key performance metrics—such as inductance and Q-factor—with an average error below 5%. It outperformed other neural architectures in accuracy. Once trained, this surrogate model replaced the time-consuming electromagnetic simulations, allowing for rapid iteration.

The Mechanism: Genetic Optimisation

Speed enables exploration. With the Transformer model acting as a fast predictor, the team deployed a multi-objective genetic algorithm. This algorithm mimics natural selection. It generates inductor geometries, tests them against the neural network, and 'breeds' the best performers. The system sought Pareto-optimal solutions, balancing conflicting objectives like bandwidth and physical footprint.

This 'data-driven modelling + intelligent optimisation' framework allowed the software to scour the design space far more effectively than human engineers or traditional solvers. It found geometric configurations that conventional methods missed.

The Impact: 100 Gbps Reliability

The results were measurable and significant. The optimised design achieved a 177% improvement in bandwidth. System-level simulations under 100 Gbps non-return-to-zero (NRZ) modulation revealed a 50% larger eye opening. A larger eye opening indicates a clearer, more distinct signal.

Furthermore, the bit error rate (BER) dropped by an order of magnitude. This reduction is vital for signal integrity in high-speed networks. The study suggests this framework is scalable. While currently applied to modulators, the methodology could likely optimise other passive components in micro and nano-photonics, reducing development cycles from weeks to days.