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

Altium schematics are captured as XML files with (.SchDoc) extension.  The file is structured to represent an electronic circuit containing various elements, such as components, nets, symbols, and annotations.  Below is an example illustrating Altium schematic file structure:

<AltiumSchematicDocument>
  <Header>
    <Version>1.0</Version>
    <Author>John Doe</Author>
    <Title>My Schematic</Title>
  </Header>
  <Components>
    <Component>
      <Designator>R1</Designator>
      <Type>Resistor</Type>
      <Value>10k</Value>
      <Pins>
        <Pin Number="1" Connection="NET1"/>
        <Pin Number="2" Connection="NET2"/>
      </Pins>
    </Component>
    <!-- More components -->
  </Components>
  <Nets>
    <Net Name="NET1">
      <Connection Pin="R1.1" />
      <Connection Pin="U1.2" />
    </Net>
    <!-- More nets -->
  </Nets>
  <Connections>
    <Wire Start="R1.1" End="U1.2"/>
    <!-- More connections -->
  </Connections>
</AltiumSchematicDocument>

2.0 Altium component libraries are expressed in XML

Altium schematics is really a graph relationship connecting components that are each in themselves an XML file.  Here is an example:

<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>

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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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Now, imagine instead of training a deep learning model on books, websites, articles, we train them on mountains of schematics files, component library files, and 3D model files that Altium has access to, and train the LLM with these data to learn the relationship amongst the components, subsystems as well as the syntax that encodes them.   

How LLMs Work:

• Training: During training, the model is shown large amounts of text and learns to predict the next word or token in a sequence. For example, given the sentence "The cat sat on the ___," the model learns to predict that the missing word is likely "mat." Over time, it learns complex patterns, such as grammar, vocabulary, and even world knowledge.
• Inference/Generation: Once trained, LLMs can be used to generate new text or complete tasks based on input prompts. Given a user’s query, the model uses its learned knowledge to generate a relevant and coherent response.

Applications of Large Language Models:

• 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 tool would require a strong understanding of the theory and application of deep learning and LLM.  It is therefore commended that Renesas first file a provision provisional patent, and then pursue the development a proof of concept prototype development with a reputable research university through corporate grants or R&D contract.  Ideally Renesas should own the IP coming out of resulting from the R&D.  If the prototype is promising, Altium can incorporate the capability into Altium their products.