Recycled SoC Detection Using LDO Degradation

Abstract

Counterfeit electronics form a major roadblock towards a safe and successful economy. An increase in globalization has led to a major increase in the total number of counterfeit products all around the world. While several methods have been designed to detect counterfeits, very few of them have been applied to the system-on-chip (SoC). The influx of a variety of components in SoCs and the conglomeration of different types of properties makes it difficult to detect counterfeit SoCs. In this paper, we aim at detecting recycled counterfeit SoCs by evaluating the degradation of power supply rejection ratio (PSRR) of a low drop-out (LDO) regulator, a principal component of the power supply of the SoC. Since the power supply is a universal component in all SoCs, this method can be considered effective for most SoCs. We apply machine learning (ML) algorithms pertaining to the family of Gaussian mixture models to classify SoCs as recycled or new. Supervised and unsupervised ML algorithms show an accuracy of up to 90% and 74% of recycled detection. We also apply stand-alone LDO PSRR degradation to train the ML algorithm and test on PSRR from embedded LDOs in SoCs. This form of semisupervised ML performed well for our previous experiments of recycled detection with stand-alone LDOs but was not able to distinguish recycled SoCs from new SoCs, thus increasing the number of false detection.

A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems

“…PSRR in the frequency domain can be modelled as in Eq. 10 [71]. The constant K depends on the feedback resistors, the load and the internal drain-to-source resistance to the small signal of the transistor.…”

“…Based on the clear influence of circuit ageing on PSRR [77], the authors introduced a technique [71], [78] based on machine learning methods that utilizes this parameter to detect recycled analog and mixed-signal chips and SoCs, respectively. As a first step, an unsupervised k-Nearest Neighbours (KNN) model was trained and tested with PSRR data from new and artificially aged devices from a single vendor.…”

“…Although Chowdhury et al claim that the proposed methods [77], [78] are to be used on ICs, they were only tested on stand-alone LDOs. Only in [71] measurements of embedded LDOs were presented, but the used models could not detect recycled chips. In addition, the reported results are based on artificially aged devices due to the lack of used chips.…”

Recycled Counterfeit Chips Detection for AMS and Digital ICs Using Low-Area, Self-Contained, and Secure LDO Odometers

“…This method was limited to standalone LDOs from different vendors. Later, this work was extended to LDOs in system-on-chips (SoCs) with less promising results [6]. In all these works, degradation of PSRR was used as a metric to measure aging of AMS chips or SoCs.…”

“…Recycled SRAM can be detected by detecting aging sensitive SRAM cells [16]. LDO degradation has been used to detect recycled system on chips (SoCs) [6]. Recycled FPGAs can be detected using exhaustive LUT path delay characteristics due to aging [3,7].…”