Evaluating Metal-Organic Precursors for Focused Ion Beam Induced Deposition through Solid-Layer Decomposition Analysis

TL;DR

This paper presents a rapid, machine learning-based method for analyzing solid-layer precursors in focused ion beam deposition, reducing testing time and resource use compared to traditional chemical analysis.

Contribution

It introduces a novel approach combining SEM imaging and machine learning to evaluate precursor decomposition, streamlining precursor screening for FIB applications.

Findings

Machine learning enhances analysis speed and accuracy.

Solid-layer irradiation analysis correlates with gas-phase deposition behavior.

Method reduces time and cost in precursor evaluation.

Abstract

The development of modern metal deposition techniques like Focused Ion/Electron Beam Induced Deposition FIBID/FEBID relies heavily on the availability of metal-organic precursors of particular properties. To create a new precursor, extensive testing under specialized gas injection systems is required along with time-consuming and costly chemical analysis typically conducted using scanning electron microscopes. This process can be quite challenging due to its complexity and expense. Here, the response of new metal-organic precursors, in the form of supported thick layers, to the ion beam irradiation is studied through analysis of the chemical composition and morphology of the resulting structures. This is done using SEM BSE/EDX along with Machine Learning data processing techniques. This approach enables a comprehensive fast examination of precursor decomposition processes during FIB irradiation, and provides valuable insights into how the precursor's composition influences the final properties of the metal-rich deposits. Although solid-layer irradiation differs from gas-phase deposition, we think that our method, can be employed to optimize pre-screen and score new potential precursors for FIB applications by significantly reducing the time required and conserving valuable resources.