Side Channel Vulnerability Analysis of Flexible Neuromorphic Circuits

The rapid advancement of flexible electronics (FE) has driven significant innovation across diverse sectors, including healthcare, wearables, smart packaging, and IoT devices, owing to their adaptability, lightweight form factor, and cost-effectiveness compared to traditional silicon-based electronics. A key computing paradigm in this domain is bespoke classifiers, where model parameters are hardcoded in neuromorphic hardware to meet strict area, power, and cost constraints. By tailoring bespoke hardware to specific tasks, these circuits achieve significant accuracy under tight resource budgets but also introduce distinct security vulnerabilities. The intrinsic flexibility of substrates, unconventional manufacturing processes, and limited protective packaging make such systems particularly vulnerable to security threats, with side-channel attacks (SCAs) being a critical concern. In this work, we systematically investigate SCA vulnerabilities in bespoke TFT-based multilayer perceptron (MLP) classifiers, considering both analog (flexible analog multilayer perceptron (f-AMLP)) and digital (flexible digital multilayer perceptron (f-DMLP)) realizations. For digital classifiers, we apply correlation power analysis (CPA), leveraging well-established leakage models from silicon-based systems. For analog classifiers, where leakage is continuous, nonlinear, and strongly influenced by device-level variability, we develop a tailored convolutional neural network (CNN)-based regression attack capable of extracting inputs from noisy power traces. Experimental results across benchmark datasets show that f-DMLPs can be compromised with 70–85% cumulative attack success rate (ASR) after ≈ 4k–5k traces using CPA, while f-AMLPs, though slower to attack initially, reach up to 90–95% ASR after ≈ 8k–9k traces with CNN-based approach.