Semiconductor making made more energy efficient

Scientists at the Department of Energy’s Lawrence Berkeley National Laboratory, in cooperation with the International SEMATECH Manufacturing Initiative, have released, for beta testing, a computer-based tool to help the world’s semiconductor manufacturing facilities ('fabs') evaluate and improve their energy efficiency.

FABS21 allows the operators of semiconductor manufacturing facilities to continuously benchmark and improve energy and water efficiency of semiconductor facilities.

Benchmarking is the process of comparing a building’s or facility’s energy and water use to those of peer facilities.

The tool draws on previous research at Berkeley into benchmarking for high-technology facilities such as laboratories, data centres and clean rooms. It also makes use of the survey methods and data collected through the Semiconductor Industry Association. Berkeley Lab researchers worked with ISMI’s Green Fab working group to validate the benchmarking methodology.

Development of FABS21 is sponsored by ISMI, the global consortium of the world’s major semiconductor manufacturers.

Users can benchmark their facilities using up to 46 different building and system level metrics, which fall into two categories. They can benchmark the overall facility energy and water efficiency, for example, as kWh/square centimetre of wafer output, and litres per square metre of manufacturing space. These metrics will help facility operators who are applying for certification in the LEED-EBOM (existing buildings operations and maintenance) rating system.

It also gives users system-level metrics, which are used for 'action-oriented benchmarking'. Users can identify potential actions to improve specific system areas such as ventilation airflow efficiency (Watts/cubic metre/minute), and chiller plant efficiency (kW/tonne). The tool has metrics for environmental conditions, ventilation, cooling and heating, process equipment and lighting and electrical systems.

Users can benchmark a facility across a set of years, as well as compare to a group of similar facilities. They can filter the peer facilities dataset based on climate zone, facility type and cleanliness level.