Generation of Documentation "Hardware Design Description for PCB" with Artificial Intelligence

 

Generation of Documentation "Hardware Design Description for PCB" with Artificial Intelligence

Autor: Francisco Prats Quílez

Introduction

Technological evolution has allowed the integration of artificial intelligence in various engineering fields, facilitating and optimizing complex processes. In the realm of hardware design for PCBs (Printed Circuit Boards), the automatic generation of design documents from technical documentation is emerging as a promising innovation. This case study describes the development and implementation of an AI-based system that, from technical documents like datasheets and project requirements, automatically generates a hardware design document for PCBs.

Objective

The main objective of this project is to develop an automated system that, using a large language model (LLM), can create a detailed hardware design document for PCBs, minimizing the time and effort required in the documentation phase and ensuring high quality in the generated content.

Development

1.     Web Interface and Document Collection

·       A web interface was developed using Vue.js to facilitate the upload of various technical documents needed for generating the hardware design document. Accepted files include datasheets, BOM (Bill of Materials), project requirements, and system descriptions.

·       Users can upload documents directly through the interface. While not all documents are mandatory, it is recommended to provide as much information as possible to improve the quality of the final document.

2.     Document Submission to Backend

·       Once uploaded, the document links are sent to the system's backend, which is designed to manage and process the received files.

3.     Document Analysis with LLM

·       In the backend, the different files are read. In the case of datasheets, the most relevant information, such as pinout, description, consumption, and configurations, is extracted using a large language model (LLM).

·       The LLM is responsible for processing and synthesizing the information efficiently, ensuring that critical data is identified and extracted correctly.

4.     Creating the Prompt for the LLM With the extracted information, a detailed and complete prompt is created.

·       This prompt includes all necessary specifications and features for the creation of the hardware design document, structured in a way that the LLM can interpret and generate a coherent and precise response.

5.     Generation of the Hardware Design Document

·       The prompt is sent to an LLM model with a large input token capacity, such as Gemini, capable of handling up to 2 million tokens. This capacity is crucial for processing the extensive and detailed prompt, as well as generating a high-quality output document that can contain around 100 thousand tokens.

·       The LLM's response is used to create a .docx document, structured according to PCB hardware design standards.

6.     Review and Modification by Experts

·       The automatically generated document is reviewed and modified by a hardware design expert. This stage is essential to ensure the accuracy and feasibility of the proposed design, as LLM models, despite being advanced, can make errors or misinterpret some technical details.

Conclusions

The integration of artificial intelligence in the process of generating hardware design documents for PCBs proves to be a powerful tool for optimizing time and resources in the documentation phase. Automating this process not only reduces the workload for engineers but also ensures a high level of detail and accuracy. However, human review remains a critical component to guarantee the quality and correctness of the final design.

Future Development

The project has significant room for improvement and future expansions. Some areas to consider include:

Inclusion of Schematics. Integrating the ability to automatically interpret electronic schematics, which would complement the hardware design document and provide a more complete view of the project.

Improvement of Prompts. Refining and optimizing the prompts used for document generation, ensuring they are as clear and detailed as possible to improve the quality of the LLM's responses.

Expansion of LLM Capabilities. Exploring the use of more advanced or domain-specific LLM models for hardware design, aiming to enhance the accuracy and relevance of the generated responses.

Integration with CAD Tools. Developing interfaces that allow direct integration with CAD design tools, facilitating the transition from the design document to practical implementation in PCB design software.