Practical sharing of semiconductor factory energy management system

Semiconductor manufacturing is a technology-intensive and capital-intensive industry, and is also a major energy consumer. For an advanced fab, the power costs may account for more than 30% of the total operating costs. Among them, auxiliary facilities such as clean room air conditioning systems, process cooling systems, and vacuum systems consume most of the electricity. Against the background of the "double carbon" goal and the country's strong support for the integrated circuit industry, energy management for semiconductor factories has risen from pure cost control to a core strategy related to corporate competitiveness and social responsibility. An effective energy management system has become an essential tool for semiconductor enterprises to realize green manufacturing and lean operation.

However, the way to construct and optimize the energy management system of semiconductor factory is not smooth. The first common misconception is "heavy monitoring, light control". Many factories deploy a large number of smart meters and sensors, which can see the energy consumption data of various departments and equipment, but the data only stays in the display and report stage, and fails to form an effective closed loop with the production process control and equipment operation strategy. Energy management has become "hindsight" and real-time intervention and predictive optimization cannot be achieved.

The second misunderstanding is the isolation of the system and the lack of deep integration with the upper-level production management system and the lower-level automatic control system. The disconnect between energy data and production plans, equipment status, and environmental parameters makes it impossible to accurately analyze energy consumption per unit of product and it is difficult to locate the process links that waste energy. For example, when an etching machine is in a standby state due to process adjustment, if its supporting cooling water system and local exhaust air are not adjusted simultaneously, huge energy consumption will be caused.

The third challenge comes from the complexity and dynamics of the semiconductor process itself. The production line operates 24 hours a day, product formulas are frequently switched, and equipment starts and stops dynamically change, which places extremely high requirements on the balance of supply and demand and rapid response capabilities of the energy system. Traditional control strategies based on fixed thresholds often appear clumsy and inefficient.

To solve these problems, it is necessary to build a four-in-one intelligent energy management system of "perception-analysis-optimization-control". First of all, there is a comprehensive and accurate "perception" layer. This requires going beyond traditional electricity, water, and gas metering, extending monitoring points to key process equipment, public power station buildings, environmental control systems, etc., and collecting multi-dimensional data such as voltage, current, flow, pressure, temperature, and humidity for analysis. Provide rich raw materials.

Second, there is a powerful and in-depth "analysis" layer. By deploying an energy management platform, massive data can be processed, stored and visualized in real time. More importantly, data mining and machine learning algorithms are used to establish correlation models between energy consumption and output, equipment OEE, and environmental parameters to achieve benchmark comparison, anomaly diagnosis, and trend prediction of energy consumption. Some domestic service providers deeply involved in the intersection of industrial automatic control and energy management have begun to provide such customized data analysis services to semiconductor customers.

Thirdly, there is a precise and automatic "optimization and control" layer. This is the closed-loop value point of the energy management system. The system automatically generates and executes optimization strategies based on the analysis results. For example, based on weather forecasts and future production plans, predict tomorrow's cooling load, and optimize the operation combination of chiller and cooling tower in advance; based on real-time particle concentration and pressure difference data in the clean room, dynamically adjust the fan frequency to achieve frequency conversion and energy conservation on the premise of ensuring environmental standards; when the system detects an abnormal increase in the pressure in the compressed air pipe network, it can automatically interlock and shut down some air compressors. These control instructions are issued through the factory's existing PLC or DCS automatic control network to realize direct intervention on the equipment.

Finally, there is a sustainable "management" closed loop. The system needs to provide complete reporting, benchmarking and assessment functions, decompose energy performance indicators into workshops and teams, and form an energy-saving culture in which all employees participate. At the same time, the system should have good scalability and be able to adapt to the factory's future production line expansion and technical transformation.

A successful case comes from a large semiconductor manufacturing company along the coast of East China. The company has introduced an integrated energy management and self-control solution. The service provider not only deployed the energy data collection network, but more importantly, deeply integrated it with the factory's original production execution system and equipment control system. Through machine learning on historical data, the system establishes energy consumption models for different products and different machine states, and develops demand control strategies based on real-time prices. After the implementation of the project, while the factory's output increased, the energy consumption per unit of product dropped by 8%, saving millions of yuan in annual electricity bills, and the return on investment cycle was significantly shorter than expected. This practice shows that the value of the energy management system lies in its deep integration with production operations and intelligent decision-making capabilities.

For semiconductor factories located overseas such as Thailand, energy management also faces unique challenges such as local power grid stability and energy price fluctuations. This requires energy management system providers to not only have excellent technology, but also have international project experience and localized service capabilities, and be able to flexibly respond to the actual situation of different regions.

To sum up, the energy management of semiconductor factories is a protracted battle that requires careful calculation and smart control. To build an effective system, we must abandon the old thinking of "just looking and not caring" and move towards a new model of "data-driven, integrated control, and intelligent optimization." When selecting a partner, you should focus on whether they have deep knowledge of industrial self-control, data analysis and algorithm development capabilities, as well as successful practices in the industry. Only in this way can energy be transformed from a cost expenditure into green momentum to enhance the core competitiveness of enterprises and win the lead in the global competition in the semiconductor industry.