RF-PSF: A CNN-Based Process Distinction Method Using Inadvertent RF Signatures

Abstract

Stochastic variation of process parameters within a die and technology-limitation-driven variation from die-to-die give rise to unique distribution patterns for manufacturing process parameters. These patterns work as a process signature that is transferred from the device level to the system level through electrical circuits and can be used to make a distinction among the processes. In this work, we propose an in-situ manufacturing process technology distinction method‘, Radio Frequency Process Specific Functions (RF-PSF)’, that uses process-specific inherent properties of an IC manifested in the transmitted radio frequency signal. Among many desirable testing criteria, RF-PSF addresses the question of fabrication with the intended process technology. This information plays an important role in modern zero-trust architecture and IC clone detection, a counterfeiting method where the IC is manufactured using a different process. An RF transmitter with RF-DAC power amplifier for QPSK modulation has been designed and simulated in 14nm, 22nm, and 65nm processes for 5 process corners (TT, FF, FS, SF, and SS) in Cadence. The simulated data have been processed in MATLAB. A Multilayer Perceptron (MLP), trained with the constellation data, provides an average accuracy of ~90% for process distinction. Realizing that (i) a higher order modulation will have even more process information and (ii) we can harness the Convolutional Neural Network’s (CNN) improved capability on pattern recognition, we can feed image-like constellation plots to a CNN to get better and consistent performance. Using the baseband constellations for 64-QAM modulated data as images, we have achieved ~100% accuracy with commonly used, pretrained CNN models (ResNet18, ResNet50, and GoogleNet) through transfer learning. The separation among 5 process corners within a process, termed intra-process variation, is also analyzed. The effect of baseband sampling rate and ADC resolution, two practical limitations in RF systems, have been explored. An extensive study has been performed on the effect of a key design parameter at the RF circuit level i.e. W/L or aspect ratio, leading to design insights, proper CNN selection, and some control parameters. This work establishes RF-PSF as a zero-power, zero-area overhead, in-situ process distinction method.