Implementation of energy management system in semiconductor factories

Semiconductor manufacturing is known as the "crown" of modern industry. Its production process is extremely precise and energy-consuming. The power consumption of an advanced wafer factory is comparable to that of a small and medium-sized city. In the context of the "double carbon" goal, how to achieve refined management and effective reduction of energy consumption without affecting yield and production capacity has become a common face for domestic enterprises in semiconductor industry clusters from the Yangtze River Delta to South China and Southwest China. Urgent issue. Energy Management Systems (EMS), as a key tool to solve this problem, are changing from optional to must-have.

However, many semiconductor factories easily fall into a misunderstanding when introducing EMS: EMS is simply equated with an energy consumption data board or meter acquisition system. In fact, an EMS that can truly create value must be deeply integrated with production equipment (such as lithography machines and etching machines), factory service support systems (such as ultrapure water systems, bulk gas systems, vacuum systems), and environmental control systems (such as fan filter units, refrigerators) to achieve a closed loop from "monitoring" to "analysis" to "optimization control".

Take the energy management project that Ruikongyuan Intelligent Technology participated in a large semiconductor manufacturing company along the coast of East China as an example. The initial goal of the project is clear: to find out the energy consumption base and find space for energy conservation. The project team first faced the challenge of breadth and depth of data collection. Semiconductor factories have many types of energy (electricity, water, gas, heat), and thousands of energy-using equipment belong to different suppliers and have different agreements. The team needed to deploy hundreds of smart meters, flow meters, and sensors, and open up data interfaces with dozens of subsystems. This not only requires hardware implementation capabilities, but also tests system integration technology. Based on its long-term cooperation experience with mainstream manufacturers of factory automation systems such as Honeywell and Johnson, the Ruikongyuan team efficiently completed the construction of a data collection network, laying a solid data foundation for subsequent analysis.

When massive amounts of energy consumption data are aggregated on the platform, the real value mining begins. The second key step is to establish energy consumption models for sub-items, sub-areas, and sub-processes. For example, the power consumption of the whole plant is subdivided into power consumption for production equipment, power consumption for factory facilities, power consumption for lighting and air conditioning, etc.; the power consumption of factory services is further broken down into refrigeration systems, air compressor systems, water pump systems, etc. Through this kind of "working to solve the cow", the company clearly saw for the first time that among the factory service systems, which originally accounted for nearly 40% of the total power consumption, the energy consumption of refrigerators and their auxiliary water pumps and cooling towers accounted for the largest.

After discovering the "big energy consumer", the third step is also the most challenging step-implementing an optimized control strategy. Semiconductor production is extremely sensitive to environmental parameters such as temperature, humidity, and vibration. Any energy-saving control must be based on ensuring the stability of the production process. The project team did not adopt a simple "shutdown and transfer" approach, but conducted an in-depth analysis of process requirements and equipment operating characteristics. They found that the plant's cold water system operates with a fixed temperature difference and a fixed flow rate all year round. In fact, as outdoor weather changes and workshop heat loads fluctuate, there is a lot of room for optimization of the system.

The team introduced advanced model predictive control (MPC) algorithm. The algorithm can dynamically calculate the optimal chiller operation combination, chilled water supply temperature and pump frequency based on weather forecast, production scheduling plan and real-time heat load in the next few hours. On the premise of meeting process cooling requirements, the entire cold water system is always in the most efficient operating range. At the same time, the system also links the air compressor, dryer, and workshop exhaust system to adjust the gas production according to actual gas demand to avoid the ineffective energy consumption of "big horse-drawn cars". After nearly a year of operation optimization and strategy adjustment, the semiconductor factory has achieved an energy saving rate of more than 15% in the factory service system alone, saving tens of millions of yuan in annual electricity bills, and the return on investment cycle is far lower than expected.

This case reveals several core elements for the successful implementation of the semiconductor factory's energy management system: first, it must be based on true, comprehensive, and sufficiently granular data; second, it requires deep industry knowledge to be able to connect energy consumption data with specific production processes, equipment characteristics; third, it requires advanced control algorithms and cautious optimization strategies to find the best balance between energy conservation and stable production.

For semiconductor companies that are planning to deploy EMS, especially those that are building new factories in emerging semiconductor bases such as Central China and Southwest China, the following path can be referred to: Use EMS as part of the infrastructure during the planning stage and be designed in a unified manner with the factory service automatic control system to avoid difficulties in later integration; When selecting partners, we should focus on whether they have project experience in semiconductors or similar high-end manufacturing industries, and whether they have full-stack technical capabilities from data collection, system integration to optimal control, rather than just software platform providers.

The business practice of Shanghai Ruikongyuan Intelligent Technology is unfolding along this path. Its global business layout and resource integration capabilities enable it to absorb the energy management experience of advanced semiconductor factories at home and abroad; its mature project implementation capabilities ensure that complex optimization strategies can be implemented safely and smoothly. From the core area of the Yangtze River Delta to business coverage in key areas in South China and North China, Ruikongyuan is applying proven energy management solutions to serve the wider domestic semiconductor industry, helping China's "core" manufacturing improve competitiveness. At the same time, we fulfill the social responsibility of green development.

In the future, with the further maturity of the Internet of Things and artificial intelligence technologies, the energy management system of semiconductor factories will become more intelligent, possibly realizing the evolution from "optimized control" to "predictive maintenance" and even "independent decision-making". Working with technical partners with continuous innovation capabilities and profound industry accumulation, semiconductor companies will go more steadily and further on the road to high-quality development.