Hiểu về chất lượng thử nghiệm trong thiết bị bán dẫn: Tổng quan
When it comes to semiconductor device testing, the primary goal is to ensure that each device meets functional and performance specifications. Testing also plays a crucial role in confirming that products will work as intended in real-world applications. However, defects in the manufacturing process can be a hindrance, making testing an essential step for quality assurance.
Why test matters: Functional and performance specifications
At its core, testing determines if a semiconductor device will meet the functional and performance specifications set during its design. Any presence of defects could prevent the device from functioning correctly, leading to failures at time zero or in the field. The testing process is designed to catch these defects.
Test coverage: The measure of thoroughness
The effectiveness or “thoroughness” of a test is measured by its coverage. For example, a test might cover 99% of the possible defects or faults in a device, meaning that 99% of a representative defect universe is detected through testing.
Test escape and DPPM: Measuring test quality
The quality of the test is often quantified by the likelihood of a “test escape”, which refers to a defect going undetected. This is typically measured in defective parts per million (DPPM) or billion (DPPB). Different industries have varying levels of tolerance for test escapes:
- Automotive and industrial sectors are highly sensitive to quality, requiring extremely low DPPM/B rates because of the safety-critical nature of their applications.
- Consumer electronics often allow for higher DPPM/B rates to balance cost and quality, accepting a small degree of defects in exchange for lower production costs.
Estimating test quality: Defect levels
To estimate test quality, several models have been developed over the years. One of the most well-known models is by Williams and Brown (IEEE Trans. Comp., 1981), which demonstrates an exponential relationship between test coverage and escape rate. This model shows that small improvements in test coverage can result in significantly larger reductions in test escapes.
Advanced defects: Subtle and difficult to detect
In advanced semiconductor technologies, defect mechanisms have become more subtle, making them harder to detect. As Moore’s Law approaches its physical limits and Dennard scaling winds down, there has been a shift toward heterogeneously integrated devices. These devices contain a growing number of transistors within a single package, pushing test times higher and complicating access to chip boundaries. This reduced accessibility lowers the controllability and observability of the device, making defects even more difficult to find.
Die matching and testing complexities
As devices become more complex, new challenges like die matching have emerged. To optimize yield, manufacturers aim to match chiplets with similar characteristics within a package. This means grouping faster or slower devices together, for example, adding a layer of complexity to the testing process.
Types of tests: Ensuring device quality
There are several different types of tests used to ensure semiconductor devices meet their specifications. A simplified test sequence for a digital system on chip (SoC) device might look like the following:
- Continuity/Contact Resistance (CRES): Ensures good contact between the tester and the chip.
- DC Parametrics: Measures voltage and current levels of the I/Os according to the device datasheet.
- Leakage Tests: Detects failures or shorts.
- Electrical Chip ID (ECID): Instantiated, recorded, and tracked throughout manufacturing and testing.
- Low Voltage and Nominal Voltage Structural Tests: Covers the device’s functionality under different voltage conditions.
- Stress Tests: Accelerates early life failures to identify potential reliability risks.
- Parametric Tests (VMIN, FMAX, IDDQ): Provides data on the device’s operating voltage, maximum frequency, and quiescent current.
Leveraging data for test optimization
Every test produces valuable data that can help optimize testing processes. These data can be broadly categorized into:
- Pass/fail – A binary outcome.
- Categorical – A discrete set of outcomes.
- Parametric – Semi-continuous measurements.
Higher resolution in test responses leads to more insightful data. For example, consider binning—classifying each device based on its performance—which can provide valuable information. Pass/fail bins might offer limited insights, while “hard bins” of approximately eight different categories provide more failure details, and then “soft bins,” with possibly hundreds of classifications, offer deeper knowledge about each device’s reason for failure.
Future trends: Test engineering and data analytics
As testing becomes more complex, test data analytics plays a critical role in resolving the balance between test quality and cost. Post-wafer probe analytics and/or predictive analytics during multiple test insertions help catch defects early, saving both time and resources. Additionally, AI/ML models are being integrated into test flows to optimize test processes further, offering faster identification of defects with minimal human intervention.
With the shift toward more advanced technologies and analytics-driven processes, and as part of addressing this trend, the Advantest ACS Real-Time Data Infrastructure (ACS RTDI) empowers customers to boost yield, enhance product quality, and accelerate time to market with cutting-edge real-time data solutions and AI/ML-driven analytics. By integrating data from across the entire IC manufacturing supply chain, the ACS RTDI platform uses low-latency edge computing and analytics within a secure True Zero Trust environment. This reduces the need for manual intervention, optimizing data usage across multiple stages and supporting customers’ databases. As a result, the ACS ecosystem helps improve quality, increase yield, enhance operational efficiency, and speed up product development and market launches.
Conclusion
The semiconductor testing process is vital for ensuring that devices meet both functional and performance specifications. As technologies advance, defects become harder to detect, requiring more sophisticated testing methods and data analytics. By leveraging comprehensive data, using advanced models, and optimizing test flows, manufacturers can maintain high product quality while controlling costs.
