Building verified multimodal training data for semiconductor AI agents
RTL simulations, timing traces, pin diagrams, and trusted hardware specifications can provide independently derived answers for semiconductor AI training data before a language model enters the loop.
4-minute read time
Why semiconductor AI needs better data
Modern AI agents are increasingly being deployed inside enterprise engineering workflows. Semiconductor experts can write and verify high-quality and accurate datasets, but they are expensive to build.
Unlike consumer chatbots, semiconductor AI agents must reason over highly specialized artifacts such as Register Transfer Level (RTL), timing diagrams, waveform traces, pin diagrams, hardware specifications, and historical debugging information.
Unfortunately, there is very little high-quality public training data covering these tasks. LLM-generated datasets scale well, generating synthetic examples much faster, but are difficult to verify because there is no reliable source of truth. This leaves an important gap:
How do we generate large amounts of trustworthy semiconductor training data without requiring semiconductor experts to manually label every example?
Our method: We derived the answer from the hardware itself. Using RTL simulations and authoritative component specifications, we generated 152,451 answer-verified samples across 61 question types and six areas of chip engineering. The dataset includes both text-based and image-based problems and was produced from approximately 44,800 base problems. The full preparation cycle took around ten days. This approach proved to be well suited to semiconductor systems, as their behavior is deterministic.
Letting hardware provide the answer
An RTL simulator can compute how signals change from cycle to cycle, given a defined hardware design and a set of inputs. Similarly, information such as pin assignments and package layouts is also defined by authoritative component specifications. These are great examples of hardware components giving us primary sources of independently derived answers before language models are involved.
Design Principles
Human expertise as the core of the process
This method is designed to reduce the need to label every individual example and to scale dataset generation without human experts. It does not, however, remove human judgment from dataset development. Humans still determine whether the resulting data is meaningful. From selecting reliable hardware designs and specifications and defining the semiconductor skills the dataset should test to designing useful question types and choosing the relevant signals, every stage requires human judgment.
After making the dataset development decisions guided by human experts, the pipeline reuses them across compatible chips, inputs, signals, and modalities. The dataset expands using a small number of high-judgment decisions into a repeatable and verifiable generation system.
Dataset generation principles
Our dataset generation strategy followed four principles.
- Ground truth should come from deterministic or authoritative sources.
- Every example should be automatically verifiable.
- Generation should scale without requiring an expert to label every example.
- The dataset strategy should support multiple agent capabilities.
- Reasoning
- Multimodal understanding
- Tool usage
- Retrieval
- Planning
How the generation pipeline works
Our dataset generation pipeline transforms trusted semiconductor engineering artifacts into verified training samples through a fully deterministic workflow. Verification applies to the final expected answer. Every retained sample has an answer derived from the output of an RTL simulation, a real component specification, and an authoritative pin mapping.
We begin with authoritative sources, including RTL designs, manufacturer datasheets, and pin diagrams, then derive ground truth directly from simulations and engineering specifications rather than from language models. These verified facts are converted into reusable question templates and rendered as both text and visual artifacts, producing diverse multimodal examples. Every sample is packaged with its task, supporting context, source artifact, verified answer, and metadata, creating a scalable, high-quality dataset suitable for training semiconductor AI agents.
Scaling Without Losing Quality
The scalability of this approach comes from the combination of reusable engineering artifacts and reusable question templates. Rather than creating each training example individually, every chip is paired with every applicable template to generate thousands of unique reasoning tasks across multiple modalities. As the chip library and template library grow, the dataset expands multiplicatively while every answer remains grounded in deterministic simulations or trusted engineering specifications.
Key insights
Rather than generating answers with an LLM, ground truth comes from trusted engineering artifacts. The LLM only converts these verified facts into natural-language training examples.- Semiconductor systems are fundamentally deterministic, and hardware specifications define deterministic constraints.
- The model does not decide what is correct. It does the task and produces an answer with a rationale, which the pipeline checks against an independently derived result.
- Human expertise remains essential to select trusted sources and design useful question types.
- Manufacturer datasheets define deterministic pin mappings.
- Pipeline reuses experts’ decisions across compatible chips, inputs, and modalities. This enables scale without giving up verifiability.
The contribution of this work is not simply a larger semiconductor dataset. It is a methodology for generating verified agent training data from deterministic engineering artifacts. By treating trusted hardware specifications and simulations as the source of truth, we can produce scalable, automatically verified datasets suitable for training enterprise AI agents operating in specialized engineering domains.
