Electronics parts assembly process technology

We are advancing manufacturing technologies by leveraging our core expertise in silicon fabrication, bonding, and adhesion processes. These foundational capabilities enable performance enhancement, cost optimization, and robust process integration for next-generation products across Toshiba's key business domains, including electronic devices, social infrastructure, and energy systems.

Advanced wet process and Si fabrication technologies

We are advancing electrochemistry-based wet processing technologies—such as electroplating and etching—and applying them to wafer fabrication to drive the creation of novel devices and improve productivity. Furthermore, by establishing methodologies for detailed analysis of interfacial reactions, we are enhancing process sophistication and developing capabilities for reaction prediction.

Bonding, adhesion and assembly technologies

We are advancing product functionality and performance, while also achieving energy savings, by identifying phenomena such as sintering and curing, modeling reactions, and optimizing heat-treatment processes. Specifically, by combining master curves that formalize reactions with thermal fluid analysis, we visualize issues in heat treatment processes and optimize processing conditions for deployment. We are also advancing the sophistication of joining and assembly processes and promoting smart factories through the use of IoT.

Design for assembly, visualization and simulation technologies

We are developing simulation technologies that visualize actual phenomena and enable prediction from manufacturing to long-term reliability. With strengths in advanced measurement and evaluation technologies for understanding physical phenomena, and in modeling complex phenomena and materials, we promote front-loading of product development to achieve both high performance and high quality at the design stage.

Smart manufacturing technologies

We are developing systems that support improvement in worker efficiency and proper quality control by collecting log data from production equipment and visualizing and analyzing process conditions. Furthermore, by utilizing AI that incorporates manufacturing know-how, we are developing methods that autonomously propose process improvements.