Scientists at DOE’s Lawrence Berkeley National Laboratory, in cooperation with the International SEMATECH Manufacturing Initiative (ISMI), are releasing for beta testing a computer-based tool to help semiconductor manufacturing facilities (“fabs”) evaluate and improve their energy efficiency.
“We developed FABS21 to allow the operators of semiconductor manufacturing facilities to continuously benchmark and improve energy and water efficiency of semiconductor facilities,” says Paul Mathew, a Staff Scientist in the Environmental Energy Technologies Division of Berkeley Lab. Benchmarking is the process of comparing a building’s or facility’s energy and water use to those of peer facilities.
FABS21 draws on previous research at Berkeley Lab into benchmarking for high-technology facilities such as laboratories, data centers, and clean rooms. The tool also makes use of the survey methods and data collected through the Semiconductor Industry Association (SIA). Berkeley Lab researchers worked with ISMI’s Green Fab working group to validate the benchmarking methodology.
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 centimeter of wafer output, and gallons per square foot 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.
FABS21 also gives users system-level metrics, which are used for “action-oriented benchmarking.” With this information, users can identify potential actions to improve specific system areas such as ventilation air flow efficiency (Watts/cubic feet per minute), and chiller plant efficiency (kW/ton). 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.