Reliability Engineering in the Era of AI: Addressing Aging Mechanisms in Deep Learning Accelerators

Read Below:

• Aging is Accelerating: Under AI workloads, BTI and HCI accelerate transistor degradation under thermal and switching stress, thus impacting timing, throughput and long-term reliability.

• Resilience is a Design Mandate: From adaptive biasing to AI-driven workload mapping, mitigating aging is now core to system architecture.

• Engineering for Longevity Starts Here: McKinsey Electronics delivers the components and support to help you build AI hardware that endures.

As deep learning systems evolve from experimental frameworks to mission-critical infrastructure, the hardware powering them, Deep Learning Accelerators (DLAs), is under pressure to deliver speed alongside long-term reliability. These chips, often deployed in autonomous vehicles, data centers, defense systems and edge devices, operate under extreme thermal, electrical and computational conditions. However, under these conditions, they age and they age fast.

Let’s explore the two most dominant silicon aging mechanisms, Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI), and how they threaten the performance and longevity of AI hardware. We’ll also review current strategies and innovations that aim to mitigate their impact and ensure sustainable, reliable AI computing.

The Silicon Aging Challenge

Modern DLAs built on advanced FinFET and GAAFET nodes (7nm and below) are highly dense, with billions of transistors switching at high frequency. As these chips handle vast tensor operations and massive data bandwidths, they begin to exhibit degradation due to physical wear-out effects in the transistors and interconnects.

Bias Temperature Instability (BTI)

BTI, especially Negative BTI (NBTI), occurs in pMOS transistors when negative gate bias at elevated temperature causes charge trapping at the gate oxide interface. This gradually increases the threshold voltage (Vth), degrading the transistor's ability to switch on time.

• Result: Slower logic paths, longer critical paths, timing margin violations.

Hot Carrier Injection (HCI)

In high-speed switching transistors, energetic carriers (electrons or holes) can gain enough energy to break through to the gate oxide, damaging the Si-SiO₂ interface. This alters the threshold voltage and transconductance.

• Result: Increased delay, higher error rates, logic instability.

Component-Level Impact in AI Accelerators

DLAs are composed of multiple functional units, each with unique switching patterns, voltage stress profiles and thermal behavior. These factors influence how and where aging manifests.

Here is a breakdown of how aging mechanisms affect key DLA components:

Aging Impact on DLA Components

This mapping is based on experimental and modeling data from FinFET and AI-specific ICs across several technology nodes.

Why This Matters Now

As AI moves toward larger-scale models and 24/7 operation, even small degradations can cause:

• Model inference errors

• Runtime faults

• Drastic power-efficiency trade-offs

• Unplanned hardware replacements

Reliability is no longer just a yield-time issue—it is a runtime operational risk. Google, NVIDIA, AMD and Apple now factor in silicon aging during DLA and SoC development.

For example:

• Google TPUv4 includes real-time thermal and voltage sensors across the die to detect stress zones.

• NVIDIA Hopper GPUs leverage adaptive voltage-frequency scaling to balance performance and transistor aging.

Mitigation Strategies in Practice

Modern AI chip designers use a mix of hardware, architectural and runtime strategies to counter BTI and HCI effects:

Circuit & Material Innovations

• High-k Metal Gates: Reduce the electric field across the gate oxide.

• Adaptive Body Biasing (ABB): Dynamically shifts Vth to balance degradation.

• Redundant Logic Units: Enables fault avoidance by bypassing degraded blocks.

Architectural Techniques

• Workload Balancing: Spreads switching activity to avoid hotspots.

• Path Rotation: Dynamically rotates critical paths across the die to even out stress.

• Idle-State Relaxation: Uses low-leakage sleep states to slow down degradation.

AI for Aging Prediction

The next frontier is using AI itself to predict and mitigate aging in AI chips:

• Telemetry-based ML models to forecast degradation from sensor data.

• Compiler-level optimization that maps critical workloads to "younger" silicon regions.

• Self-healing DLAs with runtime reconfiguration guided by predictive analytics.

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What Comes Next

Chip aging isn’t a slow process anymore; under AI workloads, it’s accelerated. If your application depends on continuous, long-term AI performance, whether it's autonomous navigation or fraud detection, you need to engineer for it today.

Expect to see:

• New RAS (Reliability, Availability, Serviceability) frameworks specifically for AI hardware.

• Silicon reliability telemetry APIs for data center scale fleet management.

• Reliability-optimized DNN compilers to co-design hardware and software lifetimes.

AI is entering an era where reliability defines scalability. As performance plateaus at physical limits, sustaining compute integrity over time will become the defining challenge for hardware architects. Aging mechanisms like BTI and HCI are no longer just academic; they are architectural design constraints. To build truly robust AI systems, we must shift from performance-first to performance-through-reliability thinking. It's time for reliability engineering to take center stage in AI chip design.

Headquartered in Dubai, McKinsey Electronics recognizes that the next frontier in AI is not just raw performance, but rather a long-term silicon reliability. As a trusted semiconductor distributor and circuit advisor across the MENA region, Turkey and Africa, we provide technical, on-ground engineering support for reliability-critical components used in AI accelerators, edge devices and industrial systems. Our expansive line card includes tier-one manufacturers, leaders in delivering components optimized for thermal stability, long lifecycle and high-reliability applications. Whether you're deploying AI at the edge or in hyperscale data centers, our team helps ensure your designs meet the evolving demands of performance-through-reliability computing.

Explore our line card here and contact us today to expedite your AI projects.