Most chips are specified once, verified once, and shipped. For years after shipping, they run in places their designers and testers can’t see: a hyperscale data center, a car on a busy freeway, a diagnostic system in a hospital. For most of the era in which semiconductors have been present in daily life, what happened with the chip after tape-out was largely invisible to the teams that made the chip.
Silicon lifecycle management (SLM) is the discipline of closing that gap. This post covers what SLM is, why demand for silicon telemetry is climbing, and how SLM maps across the chip lifecycle. It also explains why SLM belongs in the broader definition of a company’s Requirements Engineering (RE) practices, not only in the realm of test and yield analysis.
What Is Silicon Lifecycle Management?
Silicon lifecycle management is an emerging discipline for improving silicon health through the monitoring, analysis, and optimization of semiconductor devices as they are designed, manufactured, tested, and then deployed in end-user systems. SLM comprehends the value of collecting and managing a chip’s data across its entire life, beyond the point in time in the factory that it passes final test.
SLM combines three specific practices:
- Sensing. Embedded monitors inside the chip generate data about the die’s condition. These include process, voltage, and temperature (PVT) sensors, path-margin monitors, performance assessments, power consumption monitors, and built-in self-test resources.
- Aggregation. Telemetry is collected at multiple points in the lifecycle ranging from early wafer sort through final test, system bring-up, and eventually deployment in a product in the field. That data, either as a constant stream or as targeted bursts “sent home” at defined points, is brought into a common database.
- Analytics and action. The data enables decisions for the particular product: tune the design model, improve yield, adapt test limits, predict failure, or adjust operating conditions in the field. It also provides closed-loop feedback to continuously improve the next generations across the product line.
The premise of SLM is that a chip shouldn’t be a black box once it ships to an original equipment manufacturer (OEM) o Independent Hardware Vendor (IHV). It should report back on its wellness for the duration of its operation.
Why Is Demand for Silicon Telemetry Rising?
Several different pressures are converging. None of these are especially new, but only recently has infrastructure existed with capacity sufficient to make telemetry not just a theoretical benefit, but a practical option for semiconductor designers and manufacturers.
Scaling Makes Margins Thinner
Technology, design, and system scaling are all in play. More functionality lands in a single integrated circuit (IC), and more ICs (chiplets, 3D stacks) land in a single package. Each step adds interfaces and interactions that are hard to model before silicon exists.
Some Failures Only Show Up in the Field
Silent data errors (SDEs) are the clearest example. Early analyses from large hyperscale operators found SDEs affecting roughly one in 1,000 servers. Manufacturing test alone can’t catch them all: a device can have healthy margin at production test and later lose it to aging, workload stress, and environmental conditions. Hyperscale operators have also published research on using in-field monitoring to detect timing-related failures in very large data centers, failures that were previously very difficult to detect.
The Cost of a Failure is High
In a data center, a chip that silently corrupts results is more than just a semiconductor chip failure. It can become a business problem, not just a quality problem. In safety-critical industries, in-life reliability and safety evidence is essential to meet compliance requirements. Early signals of degradation in the field afford the opportunity to prevent catastrophic failures later.
Data Makes the Next Chip Better
Field and production data can feed back into design models and margins. This evidence leads to better prioritization and more complete specifications.
How SLM Maps Across the Chip Lifecycle
SLM can be described in five stages.
| Stage | What SLM Does | The Requirements Question |
|---|---|---|
| In-design | Defines and places monitors and calibrates design models against silicon behavior | What are the critical aspects of the chip to observe, and which requirement does each monitor serve? |
| In-ramp | Finds systematic issues and speeds up yield learning | Are yield and quality targets traceable to the data we collect? Are there any observations which aren’t adequately monitored? |
| In-test | Adapts test criteria and reduces test time while protecting quality | Which test limits verify which requirements, and what happens when we change the requirements? |
| In-production | Applies monitoring across high-volume manufacturing to find anomalies and catch potential issues before release | At high volume, are we still seeing the quality & performance expectations met at appropriate yield? |
| In-field | Monitors and alerts on performance, reliability, and security in deployed systems | What thresholds trigger escalation and action, and what requirement do they come from? |
Each stage produces data, but data only becomes evidence when you know the significance of what’s coming back into the closed-loop feedback system.
Traceability: Keeping Telemetry Tied to Requirements
This is where SLM and requirements engineering meet.
A chip can carry thousands of monitors. Each one consumes area, power, and design effort, and therefore cost, and each produces data that must be transmitted, stored, and interpreted. A monitor that can’t be tied to a quantifiable reason to exist is hard to defend in a design review, hard to prioritize when area is tight, and hard to interpret when its data arrives years later.
Here’s a simple example. A system requirement says a device must maintain a defined timing guardband across its profile in use. A monitoring requirement can be derived from that system requirement: the chip shall provide path-margin visibility on a specified set of critical paths.
The monitoring requirement is implemented in Register-Transfer Level (RTL) and Design for Test/Testability (DFT) output, verified during bring-up, and then, in the field, drives a threshold that triggers an alert or mitigation. When field data shows the timing guardband margin eroding faster than modeled, at a minimum that finding should flow back and change the requirements for the next generation. It might even warrant a published errata statement or a software hot fix to deployed systems.
That loop touches many artifacts and domains. Traceability is critical. It must be implemented and managed forward from the requirement to implementation and test, backward from evidence to requirement, with every link walkable both ways.
“Bidirectional” traceability is necessary but not sufficient. A single monitor isn’t watching a single unique thread. A given finding may relate to reliability, power, functional safety, security, test, and foundry process constraints, as well as the dependencies among them, not all of which can be proactively predicted.
Semiconductor teams experience traceability as a network of relationships across domains and abstraction layers, not a straight line from top to bottom. Telemetry makes this especially visible, because the same data point can matter to several teams for different reasons.
Keeping telemetry tied to requirements pays off in concrete ways:
- Prioritized monitors. Each monitor has a defined purpose and scope, so area and power trade-offs can be justified.
- Interpretable data. When a signal arrives of out-of-bounds performance, you know which requirement, threshold, and assumption to start with to explore the finding.
- Faster impact analysis. When a requirement changes, you can see which monitors, tests, and analytics rules are affected.
- Audit-ready evidence. In regulated industries, data can demonstrate that in-field monitoring connects to safety and reliability claims.
- Solid feedback loop. Field findings become new or revised requirements instead of lost lessons.
Challenges With SLM Adoption
SLM is promising, but it is neither free nor automatic.
Overhead and Placement
Monitors add design complexity, area, and power. Every monitor is a claim on scarce resources, and must be justified amidst the body of other requirements, most of which are tied to customer revenue. The most timing-sensitive paths often aren’t known until after place-and-route, which makes early placement hard.
Data Management
What to collect, how often, where to keep it, and how long it is retained are not trivial questions. Streaming telemetry at volume takes bandwidth and storage, and the data may take many hops from its ultimate location back to the chip OEM through a complex web of vendors. Also, more data isn’t automatically better data. Distinguishing a potential signal amidst a lot of noise requires the ability to look retrospectively at collected data, including its various relationships to other data where present.
Organizational Silos
Planning, design, test, measurement, validation, and manufacturing teams have traditionally worked in various degrees of isolation from one another. SLM cuts across all of them. “Ownership” of an issue may need to be distributed, which can create organizational challenges as well as technical ones.
No Universal Standard
SLM approaches remain highly use-case dependent, which makes reuse and interoperability harder. As both silicon telemetry and semiconductor products evolve, monitoring practices will evolve and potentially diverge as well.
Unclear Ownership of Intent
Monitors are often specified informally in a design specification or even a slide, with no persistent link to the requirement they serve. That limits their efficacy across the chip’s lifetime. When the underlying product requirements management practices are themselves immature, a clear and unambiguous signal from a chip monitored in-field may get stuck on its path back up to the source in either requirements or architecture.
How to Plan a Silicon Lifecycle Management Initiative
No semiconductor company develops and deploys a full fleet analytics platform on day one. There’s not a “right way” to adopt telemetry.
- Start from the questions, not the monitors. Write down what is most critical to know about the chip in production and in the field: power and performance margins, aging, workload stress, environmental conditions, and safety or security events. Those become requirements.
- Write monitoring requirements explicitly. Treat “the chip shall expose X” just as any other requirement, with quality expectations, accountable reviews, and an owner, rationale, and acceptance criteria. Write these requirements for reuse to ensure consistency across generations of a product and product line.
- Link requirements at both ends, aware of the broader web of connections. Connect each monitoring requirement upward to the system, reliability, or safety requirement it serves, and downward to the design, DFT, and test items that implement and verify it. Comprehend the web of requirements affected by any given feature or function to be able to perform impact analysis.
- Pilot on one product line, or even a part of a product. Pick a chip with a known or anticipated field-reliability or yield risk, and prove value there first. Begin with a small experiment to test the telemetry framework. Evolve and expand it based on relative risks and business value.
- Agree on data ownership and a feedback path. Decide who owns the telemetry, who reviews it, and how findings return to and evolve the requirements catalog.
How Jama Connect Supports Silicon Lifecycle Management
Jama Connect® isn’t a telemetry platform and doesn’t replace your SLM analytics or monitoring infrastructure. It provides the requirements management and traceability layer that defines the scope and success criteria for that infrastructure. When product requirements are managed in Jama Connect, traceability between specific telemetry targets and critical functionality can be tuned, enabling better predictive analysis of product functionality in context.
- A system of record for requirements. Monitoring and reliability requirements live alongside system, hardware, software, and safety requirements, with reviews and approvals in one place.
- Live Traceability™. Jama Connect describes a digital thread from high-level system requirements down to hardware and software work items, kept current as the project evolves.
- Live Trace Explorer™. A real-time view of relationships lets teams spot gaps, such as a monitoring requirement with no verification, and see which hardware blocks, software modules, and test cases a change touches.
- Jama Intelligence. Focused AI help inside Jama Connect, in the flow of engineering work. Improve requirements, generate test cases, and discover relationships within the engineering record teams already trust.
- Impact analysis and audit-ready reporting. Teams can see what’s downstream of a change and generate the requirements traceability documentation that standards in regulated industries require.
- Toolchain integrations. Jama Connect integrations include EDA, simulation, and development tools, including Jira, so requirements stay linked to the engineering work and test evidence produced elsewhere. The SLM tooling landscape is broad and still evolving, and traceability to requirements is what keeps the approach vendor-neutral.
The SLM system or platform holds the telemetry. Jama Connect holds the intent that gives that telemetry its significance, and can maintain and manage the links between the two.
Connect Silicon Lifecycle Data to Engineering Intent
Silicon lifecycle management creates more value when field evidence can inform the requirements behind current and future products. Jama Connect provides the requirements and traceability layer that connects telemetry to engineering intent, helping semiconductor teams turn what they learn in the field into better-informed next-generation products.
FAQ On Silicon Lifecycle Management
Is SLM the same as Design For Test (DFT) or Built-In Self Test (BIST)?
No. DFT and test are important inputs to a SLM framework. Many SLM implementations can reuse DFT and BIST resources. But SLM is broader than just test. SLM spans design, ramp, test, and in-field operation, and it’s about continuous monitoring and analytics, not only pass/fail screening.
Does SLM replace manufacturing test?
No, it complements and extends it. Manufacturing test finds defects present at a single point in time. SLM extends into the product’s life in use, providing early warning of issues such as aging, wear-out, and degradation that develops in the field.
Is SLM only relevant to data centers?
No. Data center reliability is a prominent driver of increased adoption of SLM, but automotive and other safety-critical applications, advanced-node SoCs, and multi-die packages all benefit from visibility into silicon health across the lifecycle.
Do we need to add hardware to use SLM?
It depends. Monitors must be designed in and over time implemented in systems that comprehend the need to expand measurement, even if the data to be gathered is unavailable or even undefined at the start. It pays to decide early what you need to observe, but at the same time to plan for future scale. Initial approaches can reuse existing DFT and test infrastructure, but without a plan for ongoing maintenance and observation of an ever-increasing volume of data, the effort may be abandoned as “unmanageable.”
How does requirements engineering fit in the SLM picture?
RE is at the start and the end of the closed-loop feedback system. Requirements define what the chip must be able to reveal about itself. Traceability ties monitors, tests, and thresholds back to those requirements, and field findings flow into the reused body of requirements that define the product line. Product requirements identify functionality that the chip is expected to enable. Telemetry that reports back on the chip’s performance over time enables better prioritization of requirements for subsequent generations.
Who should own SLM inside an organization?
There’s no single answer. SLM typically spans design, test, validation, manufacturing, and quality and reliability teams, which is exactly why a shared, well-managed, traceable set of requirements is so critical. Telemetry and the data that’s recovered will outlive most company org charts, and can persist even beyond corporate realignment, mergers, and acquisitions.
