Thermo-NAS: Thermal-resilient ultralow-cost IGZO-based Flexible Neuromorphic Circuits

The demand for next-generation flexible electronics (FE) is rapidly increasing, especially in cost-sensitive consumer markets such as smart packaging, smart bandages, drug delivery systems, RFID tags, and wearable devices. Traditional silicon based electronics, constrained by high manufacturing costs and rigid form-factor, are inadequate for these emerging applications. However, the lack of rigid packaging in FE, combined with their complex and variable operating conditions, makes them more susceptible to thermal issues, therby leading to significant performance degradation, abnormal heating, and potential risks to device reliability and safety. To address these thermal challenges, we propose a novel approach to design thermal-resilient (TR) flexible analog neuromorphic circuits (f-NCs) based on amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFTs). This cross-layer approach integrates TR circuit design for activation functions (AFs) and evolutionary algorithm (EA) based TR training using Neural Architecture Search (NAS) optimizing both the circuit-level thermal resilience and the architecture level training, ensuring robust performance of $\boldsymbol{f}$-NCs under varying thermal conditions. Experiments on 13 benchmark datasets demonstrate that thermal variations result in up to its $\mathbf{5 0. 3} \boldsymbol{\%}$ accuracy loss and the proposed evolutionary algorithm-based thermal-resilient training fully recovers this accuracy at the expense of $1.81 \times$ area and $1.39 \times$ power overhead.