Nitridation optimization, electrical reliability assessment, and AI-driven property determination of silicon nitride ceramics

Abstract

This review presents an advanced methodology for producing silicon nitride (Si₃N₄) ceramics with tailored microstructures and distinctive engineering characteristics. Si₃N₄ ceramics are typically composed of highly complex microstructures, including elongated grains dispersed amidst fine grains and grain boundaries, resulting in excellent mechanical, thermal, and electrical properties. Owing to these unique morphologies, Si₃N₄ with enhanced thermal conductivity and mechanical strength has potential practical industrial applications, particularly in insulated heat-dissipating substrates for power modules. In this review, first, the nitridation parameters for the sintered reaction-bonded Si₃N₄ method, which can be utilized to prepare high-performance Si₃N₄ ceramics, are discussed. Second, details of the dielectric breakdown in Si₃N₄ are introduced, and possible defects are suggested based on the results. Finally, artificial intelligence (AI)-based determination technologies utilizing microstructural images via convolutional neural network models are proposed, demonstrating a relatively high accuracy in the AI evaluation of the bending strength and fracture toughness.