Rethinking Chip Reliability For Harsh Conditions

As semiconductors push into environments once considered untenable, reliability expectations are being redefined. From the vacuum of space and the inside of jet engines to deep industrial automation and electrified drivetrains, chips now must endure extreme temperature swings, corrosive atmospheres, mechanical vibration, radiation, and unpredictable power cycles, all while delivering increasingly complex functionality. This shift is forcing both test and metrology processes to evolve rapidly to keep pace with the increased demand for reliability.

In the past, devices were qualified using static standards and relatively narrow use-case assumptions. But the diversity of today’s harsh applications, combined with growing system integration and heterogeneous packaging, is breaking apart those assumptions. Stress testing alone is no longer sufficient. Manufacturers now must verify performance and predict degradation under mission-specific conditions, where thermal cycling, high voltage, or vibration are normal operating states rather than corner cases. That verification begins at the wafer stage, not just on the back end.

“Everything’s AI now,” said Mike LaTorraca, chief marketing officer at Microtronic. “We’re seeing a lot more demand from places like data centers, aerospace, and defense — applications where the chips are extremely valuable and mission-critical. These customers are running low-mix, high-complexity, low-volume production, and they want to be absolutely sure they’re aging and qualifying the right devices before deployment.”

Test teams now face a dual challenge, extending the accuracy and coverage of existing protocols while also embracing new methods of system-level validation, predictive analytics, and failure modeling before, during, and after production. This increasing environmental variability is driving a shift toward more adaptive and robust qualification and reliability testing strategies on both the front and back ends.

“Customers focused on harsh environments want their qualification testing to use the same test content and data ports they’ll use in production or field operation,” said Davette Berry, senior director of customer programs and business development at Advantest. “That improves confidence that what’s tested under stress is meaningful in the real application.”

Combining SLT and burn-in for early failure acceleration
System-level testing (SLT), once considered a final safety net, is gaining ground as a necessary step for identifying failure modes that slip through earlier stages of automated test. While traditional burn-in techniques are still widely used to accelerate early-life failures, typically through elevated temperature and voltage, the rising complexity and fragility of semiconductor systems are prompting a shift toward more context-aware reliability testing.

“Burn-in is used to flush out manufacturing flaws,” said Berry. “But it’s not designed to catch the kinds of failure mechanisms that emerge when you test a full system with real workloads, especially under thermal stress.”

That’s where SLT adds value. Unlike vector-based ATE, which applies specific test patterns at the pin or logic level, SLT evaluates the chip or module in a near-final assembly, including board-level components, firmware, memory, and other system elements, more closely mimicking real-world operation. This makes it particularly effective at surfacing interaction-based failures that arise under operational stress.

The combination of system-level realism and environmental stress enables test engineers to uncover thermal instabilities, marginal contact issues, and packaging-related failures that could otherwise escape detection. In advanced packages, where heterogeneous dies with varying thermal characteristics share a common substrate, failures may stem from the cumulative effects of heat gradients, material mismatch, and workload variability across the entire module, rather than from a single element.

Shift-right, then shift-left
The integration of SLT into the test flow also is helping manufacturers make smarter decisions earlier in the product lifecycle. The idea is to “shift right” first by gathering rich failure data through realistic system-level stress, and then “shift left” by feeding that data back into the ATE wafer-level test, design, and manufacturing process.

“We’re seeing more customers shift right initially with SLT to uncover unknown failure mechanisms,” said Natalian Der, director of business strategy at Teradyne. “Then, using that data, they shift left to improve test patterns, adjust process windows, or tweak packaging materials. It’s a continuous learning loop.”

This approach is especially important when dealing with advanced packaging configurations like 2.5D interposers or vertically integrated die stacks.

“The more complex the packaging, the more value SLT provides,” adds Berry. “You’re not just testing an isolated chip. You’re testing a system. And that’s where many of the reliability issues start to emerge.”

Design-test synchronization
Another benefit of SLT is that it offers a more direct bridge between design and manufacturing. The same data ports and interfaces used in SLT often can be re-used in system monitoring during field deployment.

“In cars, for example, devices are often polled through the CAN (Controller Area Network) bus or other interfaces to check status,” said Advantest’s Berry. “If we use those same data ports in SLT, the test coverage becomes more relevant in end-use qualification. That reduces duplication and improves confidence in field reliability.”

Some companies are even extending this idea to support in-field requalification, where previously tested devices are re-qualified under operational load after board assembly, or even after deployment.

“High quality needs to be guaranteed at two levels,” says Nilanjan Mukherjee, senior engineering director for Tessent at Siemens EDA. “First, at the die-level, it is extremely important to guarantee a known good die (KGD), as the cost for throwing away a packaged part post-integration becomes prohibitive. Second, to facilitate integration of KGDs, one must carefully implement a DFT strategy both at the die and the package level that would help test/repair the high-speed interconnects (including TSVs) between dies, thereby minimizing potential failures and improving yield.”

Predictive reliability through data correlation
As chips increasingly are deployed in environments where failure is not an option, predictive reliability strategies are becoming critical. Traditional qualification methods alone are no longer sufficient. Manufacturers are now correlating data across the entire chip lifecycle, from wafer inspection and test to in-field operation, to predict and prevent failures before they occur.

“We focus on identifying the strongest die early in the line, using guard-banding and digital ink-out to eliminate the marginal or ‘walking wounded’ chips that may pass basic tests but are prone to long-term failure,” said Errol Akomer, director of applications at Microtronic. “This process is particularly critical for chips bound for automotive, aerospace, and data center markets, where longevity and documentation are paramount.”

By correlating macro-level defects across photolithography and post-CMP stages, the company builds a comprehensive history for each wafer, enabling manufacturers to rule out suspect dies before final test and packaging.

Fig. 1: Spin macro defect. Source: Microtronic

“Any visual defects in chips intended for harsh environments, such as automotive applications, should be regarded as potential threats to reliability,” said Woo Young Han, product marketing director at Onto Innovation. “While these cosmetic flaws may not initially affect the chip’s electrical performance, they can lead to reliability problems over time as the chips operate in real-world conditions. This underscores the importance of performing 100% outgoing quality assurance (OQA) visual inspections and conducting electrical tests at extreme temperatures for automotive-grade chips.”

On the other end, once a chip is qualified, embedded agents can provide real-time data throughout the chip’s lifetime, first during production testing and later during system operation. “Our technology enables both in-test decisions and in-field health monitoring,” explained Alex Burlak, vice president of test and analytics at proteanTecs. “It gives visibility into chip margin, power behavior, and performance under actual workload conditions. These are insights you can’t get from traditional test setups alone.”

What makes this convergence especially powerful is its role in closing the feedback loop. Data from Microtronic’s early wafer screening helps eliminate high-risk dies before final assembly. Once deployed, proteanTecs’ embedded agents detect subtle degradations or unexpected thermal or voltage anomalies, providing insights that can be traced back to fabrication or assembly decisions. Together, these technologies enable a more proactive approach to qualification and process control, especially in low-volume, high-reliability markets where test escapes carry outsized risks.

In essence, predictive reliability today is about correlation, linking optical inspection, embedded telemetry, and machine learning to forecast failure mechanisms in an iterative process of continual improvement. This integrated data cycle is fast becoming essential for ensuring that only the most robust chips survive the journey from wafer to deployment in extreme environments.

“As electronics continue to dominate sectors such as automotive, telecommunication, data centers, health care, and others, maintaining reliability and safety, calls for continuous monitoring of ICs throughout their lifecycle,” says Mukherjee. “Enabling technologies that will facilitate structural testing along with conventional functional testing is critical to monitoring and will help in quick resolution of potential failures.”

This telemetry-driven approach offers two major advantages during production, better outlier detection and smarter test optimization. Rather than relying solely on fixed thresholds or population-based limits, a predictive profile for each chip flags anomalies that deviate from expected behavior, even when they fall within typical test ranges.

“One chip might fall within the measured distribution, but based on its parametric signature, it’s expected to behave differently,” Burlak explained. “By comparing predicted values with actual measurements, customers can flag subtle outliers that would otherwise escape detection. That’s where the industry is headed — toward proactive reliability, not just reactive failure analysis.”

That level of granularity improves quality, but it also opens the door to dynamic performance tuning. Customers can use the same data to adjust voltage and frequency settings on a per-chip basis, optimizing for power savings or higher performance depending on application requirements.

Teradyne’s Der sees this evolution aligning with broader trends. “The industry wants smarter, faster test that doesn’t compromise quality,” she said. “If you can use embedded telemetry and machine learning to do targeted screening instead of brute-force coverage, you can reduce test cost while improving confidence.”

Bridging the gap between test and operations
The integration of test and field data creates a continuous reliability loop. This feedback can improve design, inform process tuning, and even automate parts of the qualification flow.

“Real-time monitoring lets us close the loop between predicted reliability and actual behavior,” said Gianfranco Di Marco, chief of staff and technical communication manager for the Power & Discrete Sub-Group at STMicroelectronics. “We validate our models not just with accelerated stress tests, but with field return data, allowing us to refine both test coverage and lifetime expectations.”

STMicroelectronics also is embedding telemetry features into its industrial and automotive chips, allowing customers to assess device health in real-time. “For rugged edge AI and automation, these features are essential,” said Di Marco. “They enable predictive maintenance strategies that reduce downtime and extend operational life.”

The same data can be used to identify usage patterns that correlate with failure, such as temperature spikes, mechanical shocks, or voltage transients, and refine qualification strategies accordingly.

“Common trends we monitor include thermal fluctuation patterns, vibration levels, and humidity exposure,” Di Marco said. “When those indicators deviate from expected ranges, we know we’re approaching a risk condition, even if the chip hasn’t failed yet.”

Standards and qualifications: A moving target
Whether destined for orbit, inside a car engine, or on a factory floor, chips exposed to harsh environments must pass rigorous qualification. Today, those standards vary widely by market, and they’re evolving quickly to keep up with technological complexity. But they’re also starting to converge.

“For aerospace and defense, we follow MIL-PRF-38535 and MIL-STD-883, which demand radiation tolerance, lot-by-lot qualification, and full traceability,” says Leon Gross, corporate vice president of high reliability and RF business unit at Microchip. “In automotive, we adhere to AEC-Q100 and AQG-324, where the emphasis is on process control and high-volume reliability. But increasingly, we’re seeing convergence. Automotive customers are now asking for aerospace-style documentation and mission-profile testing.”

That convergence is being driven by a shared need to anticipate failure before it happens. Traditional standards, while robust, are not always predictive. As edge devices take on more compute and AI functionality, and must last longer under more strenuous loads, designers are demanding qualification flows that reflect actual use cases.

“We’re collaborating with customers to build mission-profile-based qualification strategies,” said Gross. “That means defining realistic temperature cycles, mechanical stress patterns, and power usage profiles, then testing to those conditions rather than relying on generic specs.”

Qualification flows also are expanding to include accelerated life testing, enhanced stress models, and even in-field telemetry feedback loops. This allows engineers to validate performance under real workloads and use that data to refine predictive models.

“We compare our model predictions with actual stress test and field return data,” said STMicroelectronics’ Di Marco. “Any deviation becomes a feedback mechanism, helping us refine both test conditions and reliability expectations, ensuring continuous improving of our products.”

proteanTecs’ Burlak noted that AI-driven field telemetry is beginning to augment qualification. “You still need qualification standards, but telemetry allows you to treat reliability as a lifecycle issue, not a single-pass hurdle,” he said. “That’s where standards are going — toward more dynamic, context-aware qualification.”

Metrology, mismatch, and mechanical damage
As chips for harsh environments are subjected to more rigorous thermal and mechanical stress, even minor metrology oversights can lead to significant yield and reliability issues. That’s especially true at the wafer level, where thermal expansion, probe misalignment, or structural defects can introduce latent damage that shows up months later.

“Automotive-grade semiconductor wafers undergo electrical testing across a wide thermal range, typically from -30°C to 150°C,” said Onto’s Han. “These temperature fluctuations induce significant thermal expansion and contraction, with wafer diameters varying by over 100µm. Probe cards are engineered to thermally track wafer expansion. However, discrepancies in the coefficient of thermal expansion (CTE) between the wafer substrate and the probe card materials can lead to misalignment.”

That misalignment causes probe tips to contact unintended areas, causing mechanical damage such as scratches, pad deformation, or probe mark anomalies. To catch these issues, Onto has developed automated probe mark inspection and high-resolution imaging systems that monitor probe-induced damage in real time. Advanced pattern recognition algorithms detect anomalies before the wafer moves to the next stage, allowing for dynamic calibration of probe alignment and stress minimization.

Corrosion detection is another growing concern, particularly for aerospace and industrial applications where long-term exposure to moisture or contaminants can trigger slow degradation.

“Material and structural degradation due to corrosion is critical to monitor,” said Han. “Corrosion, which can manifest in various forms like pitting, cracking, discoloration is a major concern, especially in aerospace and automotive chips.”

These defects aren’t limited to the front side. Backside wafer handling is increasingly a point of vulnerability, especially in high-throughput or legacy tools. “We’ve seen scratched wafers, especially on the back side, where small particles or residue will cause deformation, affecting the active side die,” said Microtronic’s Akomer. “That damage might go unnoticed during standard inspection, but under repeated thermal cycles, it can become a crack or delamination site.”

Fig. 2: Examples of macro defects caused by backside contamination. Source: Microtronic

These types of mechanical and structural issues illustrate the growing need for continuous inspection and adaptive testing throughout the semiconductor lifecycle. As packages become denser and materials more diverse, even marginal physical variances can cascade into reliability failures under thermal, mechanical, or electrical stress. That’s pushing inspection technologies to move beyond static checkpoints, evolving into dynamic, feedback-driven systems that inform probe optimization, process control, and even packaging design.

Ultimately, managing reliability in harsh environments isn’t about solving a single challenge. It’s about aligning every stage of design, test, and metrology with the end-use mission profile. From macro defect detection to embedded health monitors and real-time field telemetry, each tool contributes to a layered defense against unpredictable operating conditions. The final goal isn’t just about meeting a qualification threshold. It’s ensuring resilience over years of deployment in the most unforgiving conditions the real world can offer.

Conclusion
As chips venture further into harsh and unpredictable environments, the industry is rethinking what reliability really means. Traditional qualification methods and ATE strategies remain essential, but they’re no longer sufficient on their own. The future of reliability lies in a layered approach—combining stress testing, system-level analysis, AI-driven telemetry, and dynamic feedback loops that extend from wafer to field deployment. From identifying potential trouble areas through macro defect detection at the wafer stage to understanding how embedded monitors detect aging in real time, each stage of the lifecycle now plays a role in ensuring long-term function and safety.

At the same time, test and metrology workflows must become more adaptive, integrated, and predictive. System-level test is catching failures that static burn-in misses, telemetry is blurring the lines between test and field diagnostics, and AI is turning reliability from a fixed metric into a living model. These shifts are not just about surviving harsh environments. The goal is to build a resilient semiconductor ecosystem that can anticipate, adapt, and improve with every device shipped.

For manufacturers, the message is clear — qualification doesn’t end when a test is passed. It begins when the chip powers on in the real world.

Related Reading
Chip Failures: Prevention And Responses Over Time
How to identify the causes of failures before they happen, and how latent defects and rising complexity can impact reliability and security after years of use.