KPI Monitoring for Intelligent Energy Management
Infineon is one of the world's leading suppliers of semiconductor solutions that make life easier, safer and more environmentally friendly. Microelectronics by Infineon is the key to a future worth living. With about 2,200 employees, Infineon Dresden is one of the Group's largest and most modern sites for manufacturing and technology development. The company produces over 400 different products on 200mm and 300mm silicon wafers. Infineon Dresden semiconductors are used in applications by customers in all four of the group's business areas: Automotive, Industrial Power Control, Power Management & Multimarket and Chip Card & Security.
As part of the sustainability strategy at Infineon, a key figure monitoring system was set up at the Dresden site for the energy efficiency of the equipment (EEE: Equipment Energy Effectiveness). The goal is the holistic evaluation of energy consumption and possible efficiency potentials at production plants. This enables intelligent energy management without negatively influencing processes.
There were two main challenges in the implementation. The comprehensive connection of diverse data sources and the correlation and interpretation of the data according to "process flow in equipment". Both near real-time data provision and the integration of historical data were essential for comprehensive process visualization and the formation of KPIs. The existing data was correlated according to different specifications in order to implement the following comparison options:
- between similar pieces of equipment
- of a piece of equipment at different periods of observation
- between similar production processes
Within a sequential installation, a show case was defined and set up on the basis of two pieces of equipment. For this purpose, a 200mm sputtering unit for the wafer production was selected. It consists of two tantalum chambers and one copper chamber and has an annual energy consumption of 955MWh. Apart from electricity, consumption of other media (e.g. gases) was also included in the analysis. It also aimed to analyze possibilities for increasing system utilization or reducing energy consumption during non-productive periods. The EEE KPI Monitoring System was implemented on the basis of Splunk Enterprise as well as the integration of algorithms in R and consists of multi-level analysis dashboards and control functions. In parallel, an assessment team will be formed and introduced to the system operation.
For the analysis, energy consumption values were measured as total values of the plant with Janitza UMG604, process data from APC files (equipment process monitoring) as well as RTC postings from DWH were used.
The solution provides a basis for "flow factor control for line balancing" as well as for "operating cost optimized control" ("real-time scheduling/dispatching of tools") or field-level work. The passive consumption determination based on data correlations serves as a basis for control functions. It also provides a basis for equipment assessments and the management of measures to increase energy efficiency. In addition, the solution provides an extended basis of data for decisions on cost reduction of media consumption as well as for long and short term fab simulations and equipment scheduling. Besides visualization and alarming, it is possible for the equipment engineer to do a detailed analysis down to the event and log level.
The EEE app itself has brought real benefits in terms of consumption transparency based on the energy and media KPIs. Plants and process chambers can be compared unambiguously by means of key figures, and additional consumption becomes evident. Furthermore, this raises the awareness of the equipment operator for the sustainable use of energy and resources.
Supervisor Equipment Engineering Infineon Technologies Dresden GmbH