Panasonic, IBM Japan Upgrade Chip Manufacturing Processes
The two companies team up to create a high-value-added system for manufacturing equipment to reduce engineering cost, stabilize product quality, and uplift factory productivity.

BM Japan, Ltd. and Panasonic Corporation’s subsidiary, Panasonic Smart Factory Solutions Co., Ltd., have agreed to collaborate in the development and marketing of a new high-value-added system to optimize the overall equipment effectiveness (OEE) of customers’ semiconductor manufacturing processes and to realize high-quality manufacturing.

As part of its circuit formation process business, Panasonic develops and markets edge devices and manufacturing methods that contribute to improving semiconductor manufacturing of advanced packaging. These new devices and methods include dry etching equipment, plasma dicers to produce high-quality wafers, plasma cleaners that increase metal and resin adhesion 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 a smart factory technology. Among these techniques include data analysis systems, including advanced process control (APC) and fault detection and classification (FDC), as well as an upper-layer manufacturing execution system (MES) — thus improving quality and automating production management in semiconductor manufacturing processes.

In recent years, internet of things (IoT) and 5G devices are becoming faster, smaller, and more multi-functional. This has given rise to manufacturing that is based on advanced packaging technology, in which a middle-end process, which combines the wafer process from the front-end process and the packaging technology from the back-end process, has been added between the front-end and back-end processes in semiconductor manufacturing.

Objectives of collaboration graph
Fig. 1: Objectives of collaboration
Surround Camera Radar Fusion graph
Fig 2: Expanded use of advanced packaging technology
Data Analysis System
Through this collaboration, IBM Japan and Panasonic will jointly develop a data analysis system that will be incorporated into Panasonic’s edge devices. The aim of this high-value-added system is to significantly reduce the number of engineering processes required, to stabilize product quality and to improve the operating rates of manufacturing facilities. Specifically, the companies intend to develop an automatic recipe generation system for plasma dicers, which is a new advanced packaging production method that is drawing increased attention in the semiconductor manufacturing field, and a process control system that incorporates an FDC system in plasma cleaners — equipment that has demonstrated good results in the back-end process. Going forward, the new system and IBM Japan’s MES will be connected to optimize OEE factory-wide and to realize high-quality manufacturing.

The two companies intend to develop the new system for the back-end process first, then explore an expansion of the scope to the front-end process in the future.

Features of High-Value-Added System
Advancing plasma dicers
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; which will keep processed shapes stable, resulting in a high-quality dicing process.
Panasonic APX300 Plasma Dicer
Photo 1: Panasonic APX300 Plasma Dicer (DM option)
Panasonic PSX307 Plasma Cleaner
Photo 2: Panasonic PSX307 Plasma Cleaner
Advancing plasma cleaners
FDC continuously accumulates 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.