Vibe Coding in Practice: Accelerating Semiconductor Test Applications with LLMs

Vibe coding—using large language models (LLMs) to generate software interactively from natural language prompts—offers an exciting new approach for semiconductor test development, but also poses unique challenges. Unlike traditional software engineering workflows, vibe coding shifts the developer’s role from direct code authoring to guiding and refining model-generated code. While current state-of-the-art LLMs such as GPT-4 and Claude can produce high-quality code with remarkable fluency, effective use of vibe coding still requires deep engineering skill and domain understanding, particularly when creating complex or test-critical applications. The term "vibe coding" may misleadingly suggest an effortless process, but success depends on an engineer’s ability to iteratively prompt, evaluate, and integrate model outputs into robust solutions. In semiconductor test engineering, LLM-driven coding can offer substantial productivity gains across tasks such as test program generation, diagnostic tooling, and analysis workflows. Engineers can leverage off-the-shelf hosted services (ChatGPT, Claude) or deploy open source models (Llama 3) on-premises for greater control. For domain-specific or proprietary knowledge not present in pretrained models, Retrieval-Augmented Generation (RAG) techniques can enable effective LLM use while preserving IP security. Given the variety of available options, organizations must make informed choices about their LLM coding strategy, balancing trade-offs in security, accuracy, performance, and hosting infrastructure. This paper explores practical applications of vibe coding in semiconductor test development, outlines key considerations for adoption, and presents guidance for organizations seeking to harness this emerging capability.