Composable Generation Strategy Framework Enabled Bidirectional Design on Topological Circuits

TL;DR

This paper introduces a bidirectional design framework using composable generation strategies and deep learning to automatically create and analyze topological circuits, significantly simplifying complex physical design processes.

Contribution

It presents a novel bidirectional collaborative design framework with a composable generation strategy for topological circuits, enabling automatic forward and reverse design with high accuracy.

Findings

Achieved 94% accuracy in reverse design of circuit structures.

Successfully predicted topological edge states using the framework.

Validated results through experimental PCB measurements.

Abstract

Topological insulators show important properties, such as topological phase transitions and topological edge states. Although these properties and phenomena can be simulated by well-designed circuits, it is undoubtedly difficult to design such topological circuits due to the complex physical principles and calculations involved. Therefore, achieving a framework that can automatically to complete bidirectional design of topology circuits is very significant. Here, we propose an effective bidirectional collaborative design framework with strong task adaptability, which can automatically generate specific results according to our requirements. In the framework, a composable generation strategy is employed, which involves building a shared multimodal space by bridging alignment in the diffusion process. For simplicity, a series of two-dimensional (2D) Su-Schrieffer-Heeger (SSH) circuits are constructed with different structural parameters. The framework at first is applied to find the relationship between the structural information and topological features. Then, the correctness of the results through experimental measurements can be verified by the automatically generated circuit diagram following the manufacture of Printed Circuit Board (PCB). The framework is demonstrated by achieving good results in the reverse design of circuit structures and forward prediction of topological edge states, reaching an accuracy of 94%. Overall, our research demonstrates the enormous potential of the proposed bidirectional deep learning framework in complex tasks and provides insights for collaborative design tasks.