Analyzes Power Supply Regulators to Determine if Any Chip Components Are Recycled Counterfeits
This counterfeit detection system determines whether a suspect integrated circuit (IC) or system-on-chip (SOC) component is recycled. The prevalence of counterfeit electronic components in the supply chain costs manufacturers billions and threatens the reliability of American infrastructure. Counterfeit electronics also jeopardize critical national security initiatives, as 15 percent of the replacement parts the Pentagon purchases are counterfeit. Recycled integrated circuits are the most common type of counterfeit electronic component. Available techniques for detecting recycled components require data from known authentic chips, which are usually not accessible or are not applicable to all types of frequently counterfeited electronics.
Researchers at the University of Florida have developed an inexpensive universal analysis system and algorithms for detecting any type of recycled counterfeit electronic component. The process determines if an electronic component is a recycled counterfeit by analyzing its power supply regulator, a ubiquitous element in various counterfeit chips. The universal technique will benefit electronics testing services and help secure supply chains for electronic device manufacturers, governments, etc.
Application
Universal detection of recycled integrated circuit components that exposes counterfeits to secure the international electronics supply chain
Advantages
- Detects any type of recycled electronic component across vendors, helping companies that buy and sell chips avoid counterfeits
- Runs at almost zero cost, exposing counterfeits without hurting production efficiency or inflating market prices
- Improves security in the electronic chip supply chain, minimizing electronic device failure to ensure safer infrastructure
Technology
The detection process analyzes the power supply rejection ratio (PSRR) data of a component’s low-dropout regulator (LDO), an almost universal power supply regulator, to evaluate its degradation and determine its age. An artificial intelligence machine-learning model generates an aging projection for LDOs. After evaluating the degradation of an electronic component’s LDO, comparing its degradation with the LDO aging projection can determine whether the component is recycled. The machine-learning algorithm that differentiates aged and new LDOs can be user-supervised or unsupervised. With semi-supervised and supervised algorithms, the process can effectively identify LDOs that were recycled less than 10 days prior. The universal technique can identify recycled chips at essentially no cost.
Brochure
