From Text to Test: AI-Generated Control Software for Materials Science Instruments

Abstract

Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these advancements, their application is constrained to labs with automated instruments and control software, leaving much of materials science reliant on manual processes. Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit using ChatGPT-4. Through iterative refinement, we achieved effective instrument management with minimal human intervention. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements. Finally, we integrated this AI-crafted instrument control software with a high-performance stochastic optimization algorithm to facilitate rapid and automated extraction of electronic device parameters related to semiconductor charge transport mechanisms from current-voltage (IV) measurement data. This integration resulted in a comprehensive open-source toolkit for semiconductor device characterization and analysis using IV curve measurements. We demonstrate the application of these tools by acquiring, analyzing, and parameterizing IV data from a Pt/Cr$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$ heterojunction diode, a novel stack for high-power and high-temperature electronic devices. This approach underscores the powerful synergy between LLMs and the development of instruments for scientific inquiry, showcasing a path for further acceleration in materials science.

Figures (11)

• Figure 1: Diagram depicting the AI-engineered current-voltage (IV) characterization module and parameter extraction tool described in this work. The Source Measure Unit (SMU) is connected to a control computer running a Python application developed by ChatGPT. The collected IV characterization data is subsequently processed using a high-performance differential evolution algorithm for automated parameter extraction from the IV curves, to provide insights for redesigning the device stack.
• Figure 2: Initial prompts evaluating ChatGPT's comprehension of SCPI (Standard Commands for Programmable Instruments) and its proficiency in generating Python code. The dialogue demonstrates ChatGPT's resilience to a typographical error (SCIPI instead of SCPI), underscoring its robust understanding and ability to accurately respond to queries related to programming and instrument control.
• Figure 3: ChatGPT-crafted Python class (initial architecture, before subsequent refactoring based on prompts) for interacting with a Keithley 2400 SMU, with methods for instrument connection, panel and measurement mode selection, and setting a compliance level. An example usage was also provided.
• Figure 4: Initial prompt instructing ChatGPT to create a graphical user interface (GUI) for interfacing with the instrument's control class, to assess whether it correctly comprehended its task.
• Figure 5: ChatGPT-crafted graphical user interface for a Keithley 2400 controller. The user can select the operation panel, measurement mode, source and measure varibles (voltage and current), sweep parameters (start, stop, step size), compliance, source and measurement ranges, besides NPLC and delay parameters. The data resulting from the IV sweep is plotted and can be saved through a file dialog.