Challenges And Outlook Of ATE Testing For 2nm SoCs

The transition to the 2nm technology node introduces unprecedented challenges in Automated Test Equipment (ATE) bring-up and manufacturability. As semiconductor devices scale down, the complexity of testing and ensuring manufacturability increases exponentially. 3nm silicon is a mastered art now, with yields hitting pretty high for even complex packaged silicon, while the transition from 3nm to 2nm poses additional challenges because of the paradigm shift in the process technology.

Transition from 3nm to 2nm process

Moving from 3nm to 2nm silicon technology is a big leap, packing more transistors into the same space for faster, more efficient chips. This smaller size increases manufacturing complexity, requiring advanced techniques like EUV lithography, and makes heat management, signal reliability, and power delivery more challenging. Detecting defects becomes harder, lowering initial yields. Despite higher costs, 2nm technology offers significant performance improvements, driving the next wave of tech innovation.

A key difference between 3nm and 2nm is the transition from finFET to gate-all-around (GAA) transistors. GAA transistors provide better control over current flow, reducing leakage and improving efficiency. This shift enhances performance and power efficiency but also adds complexity to design and manufacturing. Embracing 2nm is crucial for sustaining semiconductor innovation.

In terms of fabrication of 2nm process nodes, TSMC is at the forefront with risk production of its 2nm technology slated for mid-2024 and mass production targeted for Q2 2025. This 2nm process is expected to deliver 12% more performance and 25-30% greater power efficiency. The technology will support over 100 billion transistors, and with 3D stacking, this number could be even higher. While packaging technology is advancing to cater to 2.5D and 3D silicon stacking, this article focuses on ATE challenges associated with traditional 2D packaging setups.

Background and methodologies on ATE (Automated Test Equipment)

Automated Test Equipment (ATE) serves as the critical first line of evaluation for newly fabricated silicon chips. As a cornerstone of semiconductor manufacturing, ATE’s primary role is to provide a cost-effective, automated testing solution that significantly boosts device throughput. By automating the testing process, ATE not only reduces the time and expense associated with manual testing but also enhances the accuracy and reliability of these tests. This ensures that only defect-free products make it to the market. Alongside System-Level Testing (SLT), ATE offers a comprehensive testing framework that supports the mass production of chips, ensuring high quality and performance standards are consistently met.

Types of tests conducted by ATE

Structural Testing:
Purpose: To check for manufacturing defects in silicon. DFT engineers perform fault modeling and deliver test patterns that catch various types of faults.

Example: ATPG SAF, TDF, BSDL/BSCAN, LBIST.

Functional Testing:
Purpose: To verify that the electronic device operates according to its specifications.

Example: Testing a microcontroller to ensure it performs all required functions, such as data processing and signal generation.

Parametric Testing:
Purpose: To measure specific electrical parameters of a component, such as voltage, current, and resistance.

Example: Measuring the voltage levels of a power supply to ensure they fall within the specified range.

A certain combination of all the mentioned tests becomes a test flow. There are various flavors and influencing factors of the test flow, such as running at wafer-test or final-test, temperature, board design complexity, and so on.

In addition to the above tests, additional testing is carried out to ensure system-level performance and reliability of the chips:

System-Level Testing: To evaluate the performance of an entire system rather than individual components.

Stress Testing: To determine the robustness and reliability of a component under extreme conditions.

Burn-In Testing: To identify early-life failures by operating the device at elevated temperatures for an extended period.

Environmental Testing: To evaluate the performance of a device under various environmental conditions, such as humidity, temperature, and vibration.

ATE-specific challenges for 2nm

Process-driven yield issues

One of the critical aspects of test and product engineering is addressing yield issues. High defectivity in process corners, the introduction of new materials, and the use of gate-all-around (GAA) transistors introduce additional failure modes and manufacturing complexities. These complexities can lead to higher defect-limited yield (DLY) losses due to increased sensitivity to physical defects. New materials and transistor architectures, while offering performance benefits, also pose challenges in maintaining parametric yield (PLY) due to variability in electrical characteristics.

Process-driven yield loss encompasses both defect-limited and parametric-limited yields. As manufacturing processes evolve, the introduction of new techniques such as extreme ultraviolet (EUV) lithography and advanced deposition methods can result in process-induced variations. These variations can lead to defects or parameter deviations that affect overall chip functionality. At the 2nm node, maintaining high yields requires stringent process control and continuous monitoring to mitigate both types of yield losses. Innovations in process technology and tighter design margins are essential to address these challenges and ensure viable production yields.

The transition from 14nm to 2nm involves increasing complexity, with each node requiring more sophisticated yield management strategies. At 14nm, advancements like finFETs improved yields, while 7nm and 5nm nodes benefited from EUV lithography for precise patterning. The shift to GAA transistors at 3nm improved performance but added manufacturing challenges. At 2nm, maintaining yields requires advanced DFT techniques, real-time adaptive testing, and stringent process control to manage defect density and process variability.

Scan coverage challenges

At the 2nm technology node, achieving comprehensive scan coverage presents significant challenges due to increased design complexity and heightened sensitivity to defects. As transistor densities rise, ensuring that all areas of a chip are testable becomes more difficult. Traditional fault models like stuck-at faults are insufficient; advanced fault models such as delay faults, bridge faults, and soft errors must be integrated into the testing framework. Additionally, the intricate nature of 2nm designs requires sophisticated design-for-test (DFT) methodologies and improved test pattern generation to detect subtle defects and variations in transistor performance caused by process variability.

Moreover, efficient test compression techniques and robust DFT strategies, including boundary scan, built-in self-test (BIST), and logic BIST, are critical to manage the vast number of test patterns needed for high scan coverage. Process variability introduces additional challenges, as even slight deviations can significantly impact scan coverage. Adaptive testing strategies that respond to real-time feedback from the manufacturing process are essential to address these variabilities.

Memory defectivity

MBIST faces significant challenges due to increased memory circuit complexity and density. As transistor sizes shrink, the cell count on a chip rises, complicating comprehensive test coverage. This higher density means minor defects can cause critical failures, requiring advanced algorithms like March C- and Galpat algorithms for thorough testing. Additionally, the introduction of new materials and GAA (gate-all-around) transistors demands sophisticated strategies like fault models for resistive opens and shorts to address novel failure modes.

Process variability at 2nm introduces further complications, as variations in manufacturing can affect cell stability and reliability. MBIST must detect and adapt to these variations to ensure accurate testing. The heightened sensitivity of 2nm designs to process-induced discrepancies necessitates robust Design-for-Test (DFT) strategies and advancements in MBIST methodologies, such as redundancy analysis and error-correcting codes (ECC). Ensuring effective MBIST at this scale is crucial for maintaining high yields and reliability in semiconductor devices.

IO challenges

High-speed IO (HSIO) challenges are substantial due to extreme scaling and increased analog circuit complexity. Maintaining signal integrity becomes increasingly difficult as IO circuits are more prone to crosstalk and electromagnetic interference (EMI), with reduced error margins making process-induced variations impactful. Higher operating frequencies necessitate precise power delivery networks (PDNs) and signal timing, inconsistent on-chip variation (OCV) and power supply noise (PSN) affecting performance and reliability. The presence of multi-lane PCIe Gen6 (and beyond) affects signal integrity due to stringent compliance specifications, making bring-up and characterization extremely challenging.

Test-time and vector memory challenges

Increased transistor density necessitates extensive Structural, Functional, and Parametric tests. Structural tests like Automatic Test Pattern Generation (ATPG) for stuck-at and transition faults, Functional tests for verifying logic operations, and Parametric tests for measuring voltage, current, and timing parameters are critical but still insufficient. Given the novelty of 2nm technology, advanced algorithms such as March C- for memory testing and sophisticated fault models like resistive-open and bridge faults are essential for accurately detecting and isolating faults. These additional testing requirements significantly increase test time. A 2nm chip consumes nine times more test time than a 14nm chipset for the same PPA (Performance, Power, Area) metric. While smarter testflows help mitigate this gap, there is still room for improvement. The advent of AI in testflows is beginning to close this gap, enhancing efficiency and accuracy.

How to address the challenges

Advanced DFT Techniques: To tackle the increased complexity and density at 2nm, advanced Design-for-Test (DFT) techniques are essential. Implementing robust built-in self-test (BIST) and logic BIST strategies helps manage the vast number of test patterns needed for thorough coverage, including patterns for stuck-at, transition, and path delay faults. Techniques such as IEEE 1149.1 boundary scan (JTAG) and hierarchical scan architectures can segment the testing process into manageable sections, improving efficiency and coverage. Additionally, utilizing Embedded Deterministic Test (EDT) and compression methods like X-compact and LFSR reseeding can significantly reduce test data volume and time. These approaches ensure comprehensive testing while maintaining manageable test times and resource usage.

Efficient Test Compression: Given the larger number of test patterns required, efficient test compression techniques are crucial. Methods like pattern compression (also called vector memory compression) can help reduce ATE test input volume, ensuring that testing remains feasible and cost-effective by avoiding additional test insertions. These techniques help manage the increased demands on computational resources while maintaining high coverage​.

Adaptive Testing Strategies: Process variability introduces significant challenges at 2nm, necessitating adaptive testing strategies. Real-time feedback mechanisms can adjust test patterns based on observed variations, ensuring accurate and reliable testing. This approach helps mitigate the effects of inconsistencies in manufacturing and improves overall yield​.

Thermal and Power Management: Testing at 2nm requires careful management of thermal and power constraints to prevent damage to delicate structures. Implementing low-power test modes and thermal-aware testing strategies can help manage these challenges. These methods ensure that the testing process does not introduce additional risks to chip integrity​.

CAVS and DOSM for better reliability: Critical Area Verification System (CAVS) focuses on identifying and managing critical areas within a chip that are most susceptible to defects. By utilizing sophisticated software tools, CAVS analyzes the chip layout to pinpoint locations that require enhanced testing and tighter manufacturing controls. This targeted approach not only reduces the incidence of defects in sensitive regions but also optimizes the use of testing resources, leading to improved yield and reliability. Integrating CAVS into the manufacturing process helps in preemptively addressing potential defect sites, ensuring higher quality and performance of the final product.

On the other hand, Defect-Oriented Self-Monitoring (DOSM) integrates defect detection and monitoring mechanisms directly into the chip. This involves embedding sensors and diagnostic circuits that continuously track operational parameters and detect anomalies indicative of defects. Real-time monitoring through DOSM allows for immediate corrective actions, thereby preventing significant operational issues or failures. This proactive approach is crucial for maintaining high reliability, as it ensures that defects are caught early in the lifecycle of the chip. By combining CAVS and DOSM, semiconductor manufacturers can achieve a comprehensive defect management strategy that enhances both the reliability and longevity of their products, ultimately leading to cost savings and greater customer satisfaction.

Adding Additional Insertions to Test Interconnects: Adding additional test insertions during the fabrication process is an effective strategy to further enhance the reliability of semiconductor devices at the 2nm node. This approach involves embedding test points at various stages of the manufacturing process, allowing for real-time monitoring and early detection of defects. By inserting these test points, manufacturers can perform in-line testing to identify and correct defects before they propagate through subsequent fabrication stages. This reduces the likelihood of systemic failures and increases the overall yield.

Conclusion

The transition to 2nm technology introduces significant challenges in Automated Test Equipment (ATE) bring-up and manufacturability due to increased complexity and defect sensitivity. Innovations like Critical Area Verification System (CAVS) and Defect-Oriented Self-Monitoring (DOSM) are crucial for enhancing reliability by identifying critical defects and enabling real-time monitoring. Advanced DFT techniques, efficient test compression, adaptive testing strategies, and thermal management are essential to manage the complexities of 2nm nodes. Additionally, integrating additional test insertions during fabrication improves defect detection and process control. By adopting these advanced methodologies, the semiconductor industry can address the challenges posed by 2nm technology, ensuring high yields, reliability, and performance.