Executive Summary

Imagine a computer an EDA tool that can respond to a prompt prompts like “design an energy storage device with a physical dimension of roughly 3”x3”x3” a battery bank with physical dimensions of 5”x1”x3” that can stored up to 12000mAH charged by USB-C PD charger with USB charging for less than $50 $20 USD of BOM”, and then output several sets of design options as working schematics/layout files ranked according to a host of filterable selectable factors such as power consumption, cost, dimensiondimensions, and component suppliers.   

Far fetched?  With modern Deep Learning (DL) and Large Language Model (LLM), this is not a pipe-dream, but a reality.  Consider how Chat GPT is able to transform textual prompts into meaningful textual responses by training LLM on vast amounts of text from online books, articles, news.  There is no reason why LLM cannot be trained on a vast amount amounts of schematics files, PCB files, 3D model files, and generate working schematics and PCB files designs when given textual prompts.  After all, schematics file, and PCB filesdesigns, components files and the relationships among them, are all usually encoded in structured, annotated XML files–a files, a different form of language. 

Now, there are is only a handful of companies possessing enough design data to train a robust LLM, not to mention the community and reach for sustained data collectionhaving a community that will continue to produce new data, and Renesas has one of the leaders them in Altium.  Therefore, Renesas must seize should capitalize on this golden opportunity to make this vision tool a reality.

Below are additional background The sections that follow offer information on Altium design file format files generated for the most part by Chat GPT .with the prompt: "How is Altium schematics file structured?"  It serves to as a reminder the power of LLM to generate meaningful answers from when given only a simple prompt.

1.0  Altium schematic file is expressed as a language in XML

...

<AltiumComponentLibrary>
  <Library>
    <Component>
      <Name>R1</Name>
      <Type>Resistor</Type>
      <Attributes>
        <Value>10k</Value>
        <Footprint>Resistor_SMD_0805</Footprint>
      </Attributes>
      <Pins>
        <Pin Number="1" Type="Electrical" Direction="Input" />
        <Pin Number="2" Type="Electrical" Direction="Output" />
      </Pins>
      <Symbol>
        <Shape Type="Rectangle" Position="(0, 0)" Width="10" Height="10"/>
        <!-- More graphical elements defining the symbol -->
      </Symbol>
    </Component>
    <!-- More components -->
  </Library>
</AltiumComponentLibrary>

...

3.0  Large Language Model (LLM)

A Large Language Model (LLM) is a type of artificial intelligence (AI) model designed to process and generate human-like text based on vast amounts of data. LLMs are a subclass of natural language processing (NLP) models, which aim to understand, interpret, and generate human language. These models are "large" because they are trained on enormous datasets, often containing billions or even trillions of words, and they have millions to billions of parameters (the internal variables the model uses to make predictions).

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• Chatbots and Virtual Assistants: LLMs power conversational AI systems, like Siri, Alexa, or custom customer service bots.
• Content Creation: They can assist in writing articles, generating creative text, or even code generation.
• Translation and Localization: LLMs can translate text between different languages or adjust content to fit cultural contexts.
• Text-Based Search Engines: LLMs can improve search results by better understanding the intent behind user queries.
• Healthcare: LLMs can assist in medical diagnosis, summarizing patient histories, or answering healthcare-related questions.
• Legal and Financial Analysis: LLMs can process legal documents or financial reports, helping with tasks like contract review or summarization.
• Now Renesas can add automatic schematic and layout file generation to the list of successful LLM applications.

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4.0 Recommended Next Step

To realize the proposed vision require strong understanding of the theory and application of deep learning and LLM.  It is therefore commended that Renesas first file a provision patent and then pursue the development of prototype with a reputable research university through grants or R&D contract.  Ideally Renesas should own the IP coming out of the R&D.  If the prototype is promising, incorporate the capability into Altium products.

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