Bridging Hardware and AI: How Claude Code Transforms Circuit Design with Real-Time Validation

When developing hardware, the gap between concept and working prototype can be frustrating. Traditional approaches often involve writing detailed specifications, manually creating circuit diagrams, and then tediously testing each iteration. Lucas Gerads has been experimenting with a different approach: using Claude Code as a collaborative partner in hardware development, but with a crucial twist that makes the process dramatically more effective.

The Challenge of Natural Language Circuit Design

Gerads initially tried the straightforward approach: describe what you want in plain English and let Claude generate the circuit. This works reasonably well for simple designs—a basic amplifier or voltage regulator can be described and implemented without much friction. But as circuits grow more complex, the limitations become apparent.

"I found it somewhat tricky to express what I want to build in plain English," Gerads explains. The problem isn't just vocabulary; it's the inherent difficulty of describing multi-dimensional electrical relationships and trade-offs in linear text. When you're designing a power supply with specific transient response characteristics, efficiency targets, and thermal constraints, the English language becomes an awkward intermediary.

The Feedback Loop Revolution

The breakthrough came when Gerads realized that Claude Code's true power emerges when it can get immediate feedback on its work. In software development, this feedback comes from compilers, test suites, and runtime behavior. For hardware, the equivalent feedback mechanisms are simulation results and real-world measurements.

By giving Claude access to both a SPICE simulator and an oscilloscope, Gerads created a closed-loop system where the AI could propose designs, simulate them, measure the results, and iterate—all without human intervention in the middle steps. This transforms Claude from a one-shot circuit generator into an active design partner.

The Technical Setup

The implementation relies on MCP (Model Context Protocol) servers that bridge Claude Code with hardware tools. Two key components make this work:

• lecroy-mcp: An MCP server that provides Claude with access to LeCroy oscilloscopes, allowing it to capture and analyze real-world measurements
• spicelib-mcp: A wrapper around spicelib that enables SPICE circuit simulation directly within the Claude workflow

These servers expose standardized interfaces that Claude can call programmatically, treating hardware tools as APIs rather than black boxes that require human interpretation.

Real-World Benefits

The most immediate benefit Gerads noticed was in data analysis. Previously, tasks like normalizing time axes, aligning measurement data from different channels, and extracting meaningful metrics required manual effort and often relied on "eyeballing" results. With Claude handling the data processing, these tasks become automated and repeatable.

For embedded programming, the workflow becomes particularly powerful. Claude can write firmware, flash it to a microcontroller, and then use oscilloscope measurements to verify timing characteristics, signal integrity, and functional correctness. The feedback loop that might take hours of manual work collapses to minutes.

Lessons Learned and Best Practices

Through experimentation, Gerads developed several key practices that make the workflow reliable:

Oscilloscope Integration: Claude doesn't have physical awareness of your setup. You must explicitly tell it what's connected where—don't assume it can infer connections from context. This prevents the AI from making incorrect assumptions about your hardware configuration.

Data Management: Never dump raw measurement data directly into Claude's context window. Instead, save data to files and let Claude interact with those files indirectly. This keeps the context focused and prevents the AI from being overwhelmed by large datasets.

Fresh Data Only: Ensure Claude never works with stale measurement data. Each iteration should use current measurements to avoid confusion and incorrect conclusions based on outdated information.

Microcontroller Workflow: Provide Claude with explicit pinout and pinmux maps. Prepare a Makefile with standardized commands like build, flash, ping, and erase, and encourage Claude to use these rather than constructing commands dynamically. This consistency prevents errors and makes the workflow reproducible.

Scaling to Complex Projects

The demo Gerads presents is deliberately simple—a basic circuit and microcontroller setup designed to illustrate the approach rather than showcase its full potential. But the methodology scales effectively to much more complex scenarios.

Consider designing a high-speed serial interface: Claude could propose circuit topologies, simulate signal integrity using SPICE, generate test patterns, program the FPGA or microcontroller, capture real-world performance data with the oscilloscope, and iterate based on the results. Each cycle provides concrete feedback that guides the next design iteration.

The Future of AI-Assisted Hardware Design

This approach represents a fundamental shift in how we think about hardware development. Rather than treating circuit design as a purely human creative task with AI as a passive tool, it positions AI as an active collaborator that can explore design spaces, validate assumptions, and accelerate iteration cycles.

The combination of simulation and real-world measurement creates a powerful validation framework. SPICE provides theoretical performance predictions, while oscilloscope measurements capture actual behavior including parasitic effects, component tolerances, and environmental factors that simulations might miss.

As MCP servers and similar interfaces mature, the barrier between AI assistants and physical hardware will continue to erode. The workflow Gerads demonstrates today could become the standard approach for everything from student projects to professional product development, fundamentally changing how we bridge the gap between concept and working hardware.

For hardware developers interested in exploring this approach, Gerads has made his setup available in the rc-filter-demo-files repository, providing a starting point for experimenting with AI-assisted circuit design and validation.