The Problem: The Industry’s Binary Thinking

In electronic component authentication, there is a widely accepted but inherently flawed approach that can oversimplify a complex, forensic discipline: If a part shows a single counterfeit indicator, it must be suspect counterfeit. This single-indicator view of counterfeit detection is narrow, reactive, and overly simplistic.  It reduces a complex, forensic discipline into a binary decision based on a single observation:

• Surface Modification → suspect counterfeit
• Remarking → suspect counterfeit
• Residue → suspect counterfeit

Real-world authentication does not work that way. Authentication testing standards such as SAE AS6171 are designed to mitigate risk. No methodology can guarantee 100% detection of counterfeits, and no single test or observation can conclusively determine authenticity in isolation.

The reality is:

• Manufacturing processes vary, especially in legacy devices
• Legitimate rework can mimic counterfeit signatures
• Not all anomalies are created equally

A visual or material anomaly is not a conclusion. It is a starting point. Thorough authentication requires evaluating the totality of evidence including considerations for factors such as:

• Electrical performance and functionality
• External material composition (encapsulation and part marking)
• Internal construction (lead frame, wire bonding, die size and shape)
• Lot-wide consistency (uniformity across parts within a production lot)
• Statistical distribution of anomalies in the lot

Note that this article does not advocate relaxing acceptance criteria or reducing screening rigor. In high-reliability applications, conservative risk decisions remain appropriate, even when authenticity is determined beyond a reasonable doubt. It is critical to distinguish between determining authenticity and deciding acceptable risk for a use determination. These are related but fundamentally different, decisions.

Case Studies: When the Data Tells a Different Story

In each of these case studies, parts were contained and treated as suspect counterfeit until a full, multi-dimensional analysis was completed. At no point were anomalous parts released based on a single indicator.  These examples are not presented to justify anomalous conditions, but to illustrate how different types of data must be interpreted collectively to reach an accurate conclusion.

Case Study 1: M/A-COM ESMD-C15 – Residue Misinterpreted as Prior Use

A lot of M/A-COM ESMD-C15 RF components exhibited a transparent residue on the underside of 22 samples, an observation often associated with prior use or refurbishment.  Based on that signal alone, this condition would typically trigger a suspect counterfeit classification under a single-indicator model.

 

But further analysis showed:

• The material was an acrylic compound, not flux or solder-related contamination
• The distribution did not match typical use conditions
• There were no supporting indicators of prior use, such as lead wear, solder residue, or electrical deviation

When viewed in isolation, the residue suggested reuse. When viewed in context, it pointed to something else entirely: Controlled manufacturing rework to correct minor substrate defects.

Case Study 2: AMD/Xilinx XC1701LPC20I – Surface Anomalies Without Counterfeit Behavior

A lot of ~4k AMD/Xilinx XC1701LPC20I devices presented with a small percentage of samples which had visible surface sanding, an observation commonly associated with remarking and counterfeit activity.

Under a single-indicator interpretation, this condition would suggest a suspect counterfeit classification. However, a full multi-dimensional analysis of the data provides a different conclusion.

The sanding anomaly was:

• Low frequency (~0.5%), affecting only a small subset of the lot
• Consistent in form, but not systemic in distribution
• All other testing results contradicted the counterfeit hypothesis:
• Electrical testing resulted in full compliance across all samples, sanded and unsanded
• One-time programmable (OTP) memory was factory default, confirming the parts had never been previously used
• No evidence of resurfacing or blacktopping was identified on the sanded samples
• Both RAMAN and Fourier-transform infrared (FTIR) spectroscopy analysis of the marking ink and encapsulation material are consistent between sanded and unsanded samples
• Internal construction details were identical between sanded and unsanded samples, with the same lead frame, wire bonding, die shape and markings

Critically, the sanding remained visible beneath the marking, something counterfeit processes are designed to eliminate.

The conclusion was not ambiguous: The condition aligned with legitimate upstream rework, not counterfeit activity.

Case Study 3: GSI Technology GS74108ATP-10PI, Remarking That Aligned with Manufacturing Practice

A lot of GSI Technology GS74108ATP-10PI SRAM devices revealed conflicting markings after solvent testing, typically one of the strongest indicators of counterfeit remarking.

Under a single-indicator interpretation, this condition would typically be associated with suspect counterfeit. However, deeper evaluation showed:

• Marking differences aligned with up-screening faster, commercial temperature grade devices to slower, industrial temperature grade devices

In context, the most plausible explanation was: Legitimate reclassification and remarking following performance-based screening (up-screening).

Individually, each of these cases presents a classic counterfeit trigger. Collectively, they expose a much more important truth: The presence of an anomaly is not what determines counterfeit risk. The behavior of that anomaly within the full data set is what matters. 

When provided, confirmation from the original manufacturer aligned with the findings, supporting the conclusion that the described methodology produces consistent and defensible results. Importantly, these confirmations were obtained only after the analytical assessments had already been completed based on the available physical and electrical evidence. While such OEM corroboration is not typically available in many real-world investigations, its agreement with the independent analyses provides additional validation of the evidence-based approach used to assess authenticity.

3. The “Tells”: When an Anomaly Does Not Behave Like Counterfeit

Counterfeit parts rarely present as isolated inconsistencies. They present as patterns of misalignment.  These indicators inform authenticity assessment, but do not replace conservative risk-based dispositions where required:

• Low-frequency occurrence within a lot
• Absence of concealment methods (e.g., resurfacing/blacktopping)
• Material consistency across samples
• Electrical uniformity
• No evidence of prior use
• Internal detail consistency

When these conditions align, the anomaly becomes clearer from an authenticity standpoint, but not necessarily acceptable from a risk perspective.

4. Why Legitimate Parts Get Reworked (Especially Legacy Devices)

To understand why these anomalies exist, you must understand how components, particularly legacy devices, were manufactured.

• Yield Optimization: Manufacturers routinely reworked parts to recover yield, including marking correction and cosmetic refinement
• Re-Binning and Up-Screening: Devices were tested across performance thresholds, reclassified, and remarked accordingly
• Higher Process Variability: Legacy manufacturing exhibited greater variation in encapsulation and marking
• Limited Documentation: Many practices were internal to the manufacturer and not published

5. Where Legitimate Rework Happens in the Authorized Supply Chain

A common misconception is that rework only occurs at the original manufacturer.  In reality, legitimate rework can occur at multiple points within the authorized supply chain:

• Original Component Manufacturers (OCMs/OEMs): Yield recovery, marking correction, and performance-based reclassification
• Authorized Assembly, Test, and Packaging Sites: Localized rework and marking updates across distributed manufacturing
• Authorized Aftermarket Manufacturers: Support for legacy devices with minor variation in marking or materials
• Value-Added Distributors and Partners: Testing, screening, repackaging, or relabeling
• Contracted Rework Facilities: Controlled cosmetic and marking corrections performed on behalf of authorized entities

These are structured, legitimate processes – not necessarily indicators of counterfeit activity.

A documented example from Vitesse further illustrates how legitimate rework can occur outside the original manufacturer while remaining fully within the authorized supply chain. In an October 9, 2014, customer support letter, Vitesse confirmed that a lot of VSC3140VC devices had been remarked by Prime Solutions, an authorized partner, at Vitesse’s request. The work included light sanding of the original markings, application of a new coating (silvertopping), and re-marked with the same device information to restore cosmetic condition. These are precisely the types of surface modification and remarking indicators that would typically raise immediate concern and warrant a suspect counterfeit classification during initial inspection. While the parts were ultimately determined to be authentic, the initial indicators were appropriately treated as high-risk signals.

6. The Real Lesson: Counterfeit Detection Is a System, Not a Trigger

Counterfeit determination cannot be made from a single observation. It requires:

• Multi-dimensional analysis
• Correlation across independent data points
• Technical judgment informed by experience

The distinction between a false positive and a valid conclusion does not lie in the test itself, but in how the data is interpreted. Conclusions drawn from a single dimension, effectively cherry-picking isolated data points while ignoring the broader body of evidence, are inherently inconsistent with the scientific method and accepted engineering practices. Sound analysis requires integrating the full scope of available information, not selecting fragments of information.

7. Conclusion: Moving Beyond the Binary Mindset

• Counterfeit detection is not binary
• It is not deterministic
• And it is not reducible to a checklist

It is a discipline grounded in:

• Risk thresholds
• Evidence weighting
• Informed interpretation

At the same time, it is critical to distinguish between two separate decisions:

• A determination of authenticity
• A decision of acceptable risk

This approach is not about increasing risk tolerance, it is about improving the accuracy and defensibility of decisions within a conservative risk framework. A component can be determined, based on the totality of evidence, to be authentic beyond a reasonable doubt and still present an anomaly that creates an unacceptable risk for a “use” determination in a given application.  Organizations operate under different risk tolerances, and in mission-critical applications, conservative rejection of anomalous parts is often the appropriate decision, even when authenticity is strongly supported. It is entirely reasonable for an organization to decide that any anomalous condition is unacceptable for use in their system.

But it is important to recognize what that decision represents.  It is a risk decision, not necessarily a determination that the part is counterfeit. Separating these concepts enables better decision-making, clearer communication, and more effective supply chain management.

This is the difference between identifying anomalies and understanding them. When the data is ambiguous and the stakes are high, the answer isn’t found in a single test. It’s found in disciplined evaluation of all available evidence, applied within a controlled, risk-based decision framework. That’s where expertise makes the difference, ensuring that decisions are not only technically sound, but aligned with the level of protection customers expect in high-reliability applications.