For more information, contact:

Richard Beck
Advanced Energy Industries, Inc.
John Field
Symphony Systems

Advanced Energy and Symphony Systems Roll Out Suite of Productivity Tools for the Chip Industry

Proven System Enables Fabs to Delay New Tool Investments by Increasing Asset Productivity


Fort Collins, Colorado and Campbell, California (April 17, 2001)--Advanced Energy Industries, Inc. (Nasdaq: AEIS) and Symphony Systems, a leading supplier of network-based applications and open-architecture software solutions for the semiconductor industry, announced today a new suite of applications designed to dramatically improve the performance of equipment used to manufacture chips—the EPMTM Equipment Productivity Manager. This new product, which addresses productivity concerns for chipmakers, semiconductor equipment manufacturers and sensor makers alike, performs a multitude of tasks from assisting maintenance of tools in the cleanroom to web-enabled troubleshooting and analysis.

In March 2000, Advanced Energy (AE) and Symphony formed a strategic partnership to deliver an advanced network-based infrastructure that seamlessly connects people, equipment, and information to form a global resource management system that will dramatically improve overall equipment productivity. Under this agreement, both AE and Symphony will develop, market, and support these products for semiconductor OEMs and end-users, while AE will exclusively distribute these products to the semiconductor industry as a whole.

The EPM suite is a comprehensive offering that provides network connectivity, web-based automated data collection, a high-performance, real-time database for time-series parametric data, and an integrated suite of data analysis applications. When the tool is connected to the network, multiple applications can share the tool and database, communicating through open object interfaces, such as ActiveX controls, and ODBC. The EPM suite provides the platform for e-diagnostic and APC solutions. These include network connectivity, data collection and data archiving from key sources including process equipment, sensors and subsystems. Also, the EPM suite has open architecture allowing users to rapidly build their own applications.

This announcement follows on the heels of Symphony and Advanced Energy's recent introduction of the eSES™ External Symphony Equipment Server, a key component of the new software suite. The eSES offers the hardware and software platform for complete tool integration required by engineers and suppliers, while facilitating e-diagnostics. The web-enabled (Intranet and Internet) eSES connectivity solution combines equipment data with sensor data inputs, providing one interface for all users and applications.

In addition to the eSES product, the EPM suite includes: the Data Collection Manager (DCM™) for advanced data collection capability; Aspen Technologies' high-performance real-time InfoPlus.21™ (IP.21) database; Aspen Technologies' Process Explorer™ viewer for data presentation and analysis; the Symphony Equipment Tuner for analysis and management of data, events, and alarms; and connectivity hardware.

Dr. Keith Bennett, Symphony president and chief executive officer, points out that customers realize both a financial and competitive advantage with the EPM solution's single integrated package and its breadth of capabilities. "The use of the new EPM suite allows significant cost savings and the extension of soon- to-be-obsolete technologies. For example, an engineer at a customer’s site determined using Symphony's process graphic analysis tool that an etch time in a particular process can be reduced from 55 to 40 seconds without affecting wafer quality. When applied to each of the 25 wafers on each cassette, such 'fine tuning' of the process saves a quarter of a million dollars a year just on this recipe."

The EPM product provides a variety of troubleshooting tools for complex problems on all types of tools, including multiple chamber systems. The online chamber-level tool data accelerates troubleshooting and maintenance, and advanced data retrieval and analysis tools significantly decrease mean time