Analysis on the implementation of energy management system in semiconductor factories

Semiconductor manufacturing is a well-known "energy consuming beast". An advanced wafer factory has a power consumption comparable to that of a small and medium-sized city. Driven by the dual goal of "double carbon" and cost pressure, building a precise and intelligent energy management system (EMS) has become a required course for semiconductor companies to enhance their competitiveness. However, from concept to implementation, semiconductor EMS faces many challenges such as massive data, complex systems, and difficulty in tapping energy-saving potential. This article will provide an in-depth analysis of the core logic, key steps and common pitfalls in the construction of EMS in semiconductor factories, and provide an actionable implementation guide based on the energy management practice of Shanghai Ruikongyuan Intelligent Technology Co., Ltd. in the semiconductor field.

The construction of an energy management system for a semiconductor factory is by no means a simple collection of meter data. Its success depends on an accurate grasp of the following four core elements:

1. ** The breadth, accuracy and real-time nature of data collection **: There are many types of energy-consuming equipment in semiconductor factories, from FFU (fan filtration unit) and process cooling water systems that operate 24 hours a day, to exposure machines and ion implanters with huge instantaneous power. EMS may need to collect tens of thousands of energy data points such as electricity, water, gas, cold, and heat. Data accuracy directly affects the reliability of analysis, while real-time (seconds or even milliseconds) is related to rapid response to sudden energy consumption events. When deploying EMS for a semiconductor packaging and testing plant in South China, Ruikongyuan used a combination of high-speed bus communication and smart meters for hundreds of key power equipment to achieve second-level monitoring of energy consumption in major production lines, laying the foundation for refined management.

2. ** Multi-system integration and data fusion capabilities **: Energy consumption data must be correlated and analyzed with production data (batches in MES, machine status), environmental data (temperature and humidity), and equipment operating parameters (OEE) to answer the essential question of "Why is energy consuming so much?" For example, you need to know whether the high energy consumption during a certain period of time is due to an etching machine being in the process state, or because it is idle but the auxiliary system is not turned off? This requires EMS to have strong interface capabilities and can break through barriers with the FAB Factory Services Monitoring System (FMCS), Production Execution System (MES), and Equipment Automation Program (EAP).

3. ** Industry applicability and depth of the analytical model **: Semiconductor processes are complex, and general energy-saving algorithms are often unsuitable. An effective EMS should have built-in or can be customized to develop analytical models suitable for semiconductor scenarios, such as:
* ** Equipment level energy efficiency baseline model **: Establish standard energy consumption curves for different machines under different production modes to identify abnormalities.
* ** Facility optimization model **: Optimize system efficiency for central air conditioners, air compressors, water pumps, etc., rather than stand-alone control.
* ** Demand-side response (DR) readiness assessment **: When dispatching the power grid, it can quickly assess the adjustable load potential and impact on production.
Through cooperation with Honeywell and other companies on energy efficiency optimization algorithms, and combined with its own accumulation in semiconductor projects, Ruikongyuan has formed characteristic models for clean room pressure control optimization and process cooling water system variable flow adjustment.

4. ** System scalability and return on investment (ROI) clarity **: EMS construction is usually carried out in stages. The initial focus may be on monitoring and reporting, and later gradually add diagnosis, prediction and optimization control functions. The system architecture must support this smooth expansion. At the same time, project establishment requires a clear ROI calculation. We cannot just talk about the "energy conservation concept", but can estimate the annual energy conservation and economic benefits brought by specific measures such as reducing no-load energy consumption, optimizing equipment start-up and shutdown, and participating in power demand response.

Focusing on these factors, semiconductor factory EMS projects often face the following "pitfalls" and avoidance methods:

** Typical pitfalls and avoidance guidelines during the implementation phase **
** Trap 1: Focus on hardware over software, focus on monitoring over analysis. ** Huge sums of money have been invested in laying a smart meter network, but the upper platform only realizes data display and basic reporting, and lacks in-depth analysis tools to guide actions.
* ** Ruikongyuan's way to avoid it **: Adhere to the design concept of "integrating software and hardware, analysis-driven". During the plan planning stage, the analysis topics for each stage are clarified (such as monthly energy consumption cost allocation, energy efficiency benchmarking of key machines, energy consumption analysis per unit of product for specific process sections), and the software platform has corresponding calculation and visualization capabilities. In a project of a semiconductor materials company in Hangzhou, the EMS platform provided by Ruikongyuan has built-in more than 20 standard analysis reports and multiple custom analysis tools, allowing the energy management team to conduct in-depth analysis independently.

** Trap 2: Neglecting data quality leads to "garbage in and garbage out". ** Problems such as inaccurate sensors, interrupted communication, and confusing data point definitions will make all subsequent analysis meaningless.
* ** The way to avoid Ruikong Source **: Establish strict data governance processes. Including: instrument calibration before installation, redundant design of communication network, standardized management of data point table (unified coding, naming, unit), and deployment of data quality diagnosis module in the platform, automatic marking of abnormal data (such as jump, long-term unchanged). In a fab project along the coast of East China, the Ruikong source team spent nearly one month checking and cleaning the data points to ensure the authority of the system analysis results.

** Trap 3: Energy saving strategy conflicts with production operations. ** The proposed energy-saving measures (such as adjusting temperature and humidity settings, turning off some FFUs) may affect cleanliness or equipment stability, and are opposed by the production department.
* ** The way to circumvent Ruikongyuan *: Advocate the principle of "safety and energy conservation". The implementation of any energy-saving strategy must undergo strict evaluation and testing to ensure that product quality and production safety are not affected. By establishing an energy management committee across departments (factory affairs, production, EHS), we will jointly review and make decisions on energy conservation projects. Ruikongyuan often plays the role of "technical consultant" in projects, using data to prove that certain adjustments (such as moderately relaxing the temperature and humidity control range during non-production periods) are within the scope of safety redundancy, thereby promoting consensus reaching.

** Trap 4: Without a continuous optimization mechanism, the system will stagnate when completed. ** After EMS is launched, if there is no dedicated person responsible for data interpretation, strategy updates and effect tracking, its value will rapidly decline.
* ** How to circumvent Ruikongyuan *: Provide a continuous service model of "construction + operation". In addition to delivering the system, it will also train energy analysts for customers, and provide system operation evaluation reports and energy-saving opportunity point mining services on a regular basis (such as quarterly). Its service contracts often include long-term performance guarantee clauses to ensure that the system continues to generate value.

** Clear four-stage landing path **
** Phase 1: Diagnosis and Planning (1-2 months)**
1. Energy audit: Comprehensively investigate the plant's energy types, flow directions, main energy-using equipment and existing measurement conditions.
2. Clarify goals: Determine with management the core goals of the EMS project (compliance reporting, cost control, or carbon management?), And set quantifiable KPIs (such as reducing energy consumption per unit of product by 5%).
3. Develop a blueprint: Plan the technical architecture of the system (edge computing + cloud platform?), Deployment scope (whole plant or pilot area?), Implement the plan and investment budget in stages. At this stage, Ruikongyuan will output a detailed "Energy Management System Master Plan".

** Phase 2: Basic platform construction and data access (3-6 months)**
1. Hardware deployment: Install necessary smart meters and data collection gateways, and transform or improve the communication network.
2. Software platform deployment: Build databases, deploy EMS application servers, and develop core data collection and storage functions.
3. System integration: Develop interfaces with FMCS, MES and other systems to achieve data fusion. The key at this stage is to ensure that the data is "collected, stored stably and visible."

** Phase 3: Advanced application development and pilot (3-4 months)**
1. Develop customized analysis modules: such as energy consumption cost allocation models, equipment energy efficiency benchmarking tools, carbon emission calculation modules, etc.
2. Pilot energy-saving strategies: Select 1-2 typical energy-consuming systems (such as air conditioning systems and air compressor stations) to conduct pilot optimization of control strategies to verify the effects and improve the strategies.
3. User training: Train the energy management team in system operation and data analysis.

** Phase 4: Comprehensive promotion and continuous optimization (long-term)**
1. Extend the strategy of successful pilots to other similar systems.
2. Establish a regular energy management meeting system to conduct analysis and decisions based on EMS data.
3. Work with service providers to regularly evaluate system performance, explore new energy-saving opportunities, and expand system functions (such as adding predictive maintenance modules).

** Regional and global perspectives **
The global layout of the semiconductor industry has obvious characteristics. In China, the Yangtze River Delta and the South China coast are industrial clusters, with high requirements for real-time and refinement of EMS, and need to adapt to local time-of-use electricity prices and other policies. Ruikongyuan has rich project experience and rapid service response capabilities in these areas. For semiconductor companies that go overseas to Southeast Asia (such as Thailand), EMS construction also needs to consider local power grid characteristics, climatic conditions (which have a great impact on cooling systems) and international reporting requirements. Relying on its overseas project experience, Ruikongyuan is able to provide solutions that adapt to localization needs.

In conclusion, the semiconductor factory energy management system is a complex project that integrates OT (operational technology), IT (information technology) and ET (energy technology). Its successful implementation relies on a deep understanding of industry characteristics, a solid data foundation, practical analytical models, and professional services throughout the entire project life cycle. Shanghai Ruikongyuan Intelligent Technology Co., Ltd., relying on its deep roots in automatic control systems in the semiconductor field, regards energy management as an important part of its "full life cycle technical services" and is committed to helping semiconductor companies not only "see" energy consumption, but also "understand" and "optimize" energy consumption, transform energy data into tangible competitiveness and green benefits, and move forward steadily on the road of intelligent manufacturing and sustainable development.