IBM Japan, Ltd. and Panasonic Corporation’s subsidiary, Panasonic Smart Factory Solutions Co., Ltd, have agreed to collaborate to develop and market a new high-value-added system to optimize the effectiveness (OEE) of customers’ semiconductor manufacturing processes and to realize high-quality manufacturing.
Under the collaboration, IBM Japan and Panasonic will jointly develop a data analysis system that will be incorporated into Panasonic’s edge devices. The system is intended to reduce the number of engineering processes required, stabilize product quality, and improve the operating rates of manufacturing facilities.
As part of its circuit formation process business, Panasonic currently develops and markets edge devices and manufacturing methods that contribute to improving semiconductor manufacturing of advanced packaging. These include dry etching equipment, plasma dicers to produce high-quality wafers, plasma cleaners, and high-accuracy bonding devices. This expertise will be combined with techniques and technology that IBM Japan has developed for semiconductor manufacturing to help Panasonic create smart factory technology. These include a data analysis system including advanced process control (APC) and fault detection and classification (FDC), as well as an upper-layer manufacturing execution system (MES).
The computing algorithm jointly developed by the two companies enables customers to enter their desired dicing shape (etching shape), which varies from product to product, and automatically generate equipment parameters consisting of several hundred combinations. This feature is expected to significantly reduce product launch times and engineering costs. It can also be applied to the APC system, which automatically adjusts equipment parameters according to varying processing quality from front- and back-end processes. This will in turn keep processed shapes stable, resulting in high-quality dicing.
The fault detection and classification system continuously accumulate operational data from operating manufacturing equipment, detects failures through its own data analysis method, and enables the condition of equipment to be interpreted automatically. This feature generates equipment maintenance target areas and frequency needs, forecasts and prevents failures, optimizes maintenance scheduling, reduces equipment downtime, and improves operating rates.