Invisible Variations That Define Yield ：Practical Applications of SPC in Semiconductor Manufacturing

·Process Control Methods for High Yield and Stable Volume Production：

1. What Is the Real Risk in Semiconductor Manufacturing?

In semiconductor manufacturing, the most dangerous issues are often not obvious out-of-spec events, but invisible and persistent process variations.

Wafer fabrication involves hundreds to thousands of tightly coupled process steps, each operating within extremely narrow process windows. Even a minor parameter shift can be amplified through downstream processes, eventually resulting in:

• Yield degradation

• Parameter distribution drift

• Large-scale scrapping of high-value wafer lots

This reality determines that the semiconductor industry cannot rely on end-of-line inspection to ensure quality. Instead, it must continuously answer two critical questions during production:

• Is the process stable?

• Is the process still under control?

Statistical Process Control (SPC) exists precisely to address these questions and has become a foundational capability in modern semiconductor manufacturing.

2. The Role of SPC in the Semiconductor Industry

Unlike traditional manufacturing, SPC in semiconductor fabs is not merely a statistical tool used by quality departments. Instead, it serves as:

• A daily monitoring method for process engineers

• A key reference for equipment and process condition assessment

• A critical input for yield management and production release decisions

In practice, SPC is typically integrated with MES/EAP/FDC/APC systems, forming a comprehensive process control framework that supports:

• Early identification of process drift

• Proactive exception handling in advance

• Support process and equipment decisions

3. Typical SPC Application Scenarios in Semiconductor Manufacturing

3.1 Lithography: Core Control of Process Windows

Lithography is one of the most yield-critical steps in semiconductor manufacturing. SPC is commonly used to monitor:

• Critical Dimension (CD)

• Overlay 

• Dose

• Focus

Given the extreme sensitivity of lithography parameters to yield, semiconductor fabs focus heavily on subtle trend shifts rather than obvious limit violations. Therefore, SPC applications often combine:

• I-MR control charts

• EWMA and CUSUM trend detection methods

to enable early detection of ”chronic loss of control“.

3.2 Etching: Ensuring Uniformity and Stability

In etching processes, SPC is primarily applied to monitor:

• Etch depth

• Line width variation

• Within-wafer and wafer-to-wafer uniformity

Continuous SPC monitoring helps engineers identify:

• Chamber condition changes

• Consumable aging and contamination risks

• Process parameter drift

thereby reducing the risk of batch-level excursions.

3.3 Thin Film Deposition (CVD / PVD / ALD): Consistency Management

Typical SPC monitoring parameters in deposition processes include:

• Film thickness

• Refractive index

• Resistivity

• Uniformity

SPC is used not only for single-tool stability control, but also widely applied in:

• Tool matching across multiple equipment sets

• Maintenance and cleaning interval optimization

3.4 CMP (Chemical Mechanical Planarization): Preventing Over-Polish and Under-Polish

CMP processes are characterized by high process noise and complex parameter coupling. SPC monitoring focuses on:

• Removal rate (RR)

• Surface roughness

• Planarity metrics

By applying SPC, fabs can distinguish random variation from systematic drift, preventing long-term yield loss caused by cumulative deviations.

3.5 Electrical Testing and Yield Monitoring

In front-end manufacturing, SPC is applied not only to process parameters, but also extensively used for:

• Monitoring key electrical characteristics

• Analyzing yield trend indicators

This allows engineers to trace yield anomalies upstream to specific process steps, enabling faster root-cause identification.

4. Key Characteristics of SPC in Semiconductor Manufacturing

Compared to traditional manufacturing, semiconductor SPC exhibits distinct characteristics:

4.1 High Frequency, Small Samples, Large Data Volumes

• Single-point or very small subgroup sampling

• High-frequency monitoring

4.2 Complex Data Distributions

• Non-normal distributions are common

• Skewed and long-tailed characteristics frequently observed

As a result, practical SPC applications often require a combination of:

• Data transformation methods

• Trend-based control charts

• Non-normal analysis strategies

4.3 Focus on Trends Rather Than Single-Point Violations

In semiconductor manufacturing, the most significant risks typically arise from:

Long-term, gradual, and continuous process drift

Therefore, the core value of SPC lies in early trend detection, rather than reacting only after parameters exceed control limits.

5. Core Value Delivered by SPC for Semiconductor Enterprises

Through systematic SPC implementation, semiconductor manufacturers can:

• Detect process instability early and protect yield

• Reduce the risk of scrapping high-value wafers

• Support equipment maintenance and process optimization decisions

• Improve consistency and stability across tools and production lines

In advanced process nodes, SPC has become a key reference for process release and stable mass production.

6. Conclusion: SPC as the “Invisible Defense Line” of Semiconductor Manufacturing

In the semiconductor industry:

Invisible variations are often the greatest risk.

SPC is not merely a set of statistical charts, but a comprehensive process control methodology designed to:

• Continuously monitor process conditions

• Detect abnormal trends at an early stage

• Safeguard yield and stable volume production

Those who can identify process drift earlier are better positioned to protect yield—and maintain long-term competitiveness.