The near-sensor paradigm is widely used in the artificial Intelligence of Things (AIoT) and other emerging applications that require high-performance digital circuits to perform embedded control and signal-processing calculations. This work presents a low-temperature polycrystalline silicon and oxide (LTPO) CMOS-based large-scale integrated circuit (IC) design approach to the flexible sensing and processing system via a unified high-accuracy compact model for LTPO thin-film transistors (TFTs), realizing circuit simulations and reliability evaluations. Grounded in multiple-trapping and releasing theory, the compact model is developed for both low-temperature poly-Si (LTPS) and indium-gallium-zinc oxide (IGZO) TFTs, with an error of much less than $100~\mu \text{V}$ for surface potential. This study delves into the significant aging effect of the positive bias temperature instabilities (PBTI) in IGZO TFTs using calibrated multivariable kinetic equations. The findings suggest that this approach has the potential to enhance the compact integration of sensing and processing systems through the use of advanced LTPO technology ( $\sim 6~\mu \text{m}$ ). Furthermore, LTPO-CMOS-TFTs are used to implement digital circuits, achieving energy efficiency that is $3\times $ higher than the state-of-the-art flexible processors. Compared with widely used pseudo-CMOS logic, complementary logic achieves the improvements of energy consumption and speed by the factors of 122.7 and 3.19, respectively.

中文翻译：

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用于大规模集成电路驱动的灵活智能传感系统的老化感知 LTPO DTCO

近传感器范式广泛应用于人工智能物联网 (AIoT) 和其他需要高性能数字电路来执行嵌入式控制和信号处理计算的新兴应用。这项工作提出了一种基于低温多晶硅和氧化物 (LTPO) CMOS 的大规模集成电路 (IC) 设计方法，通过 LTPO 薄膜晶体管 (TFT) 的统一高精度紧凑模型来实现灵活的传感和处理系统），实现电路仿真和可靠性评估。基于多重捕获和释放理论，该紧凑模型专为低温多晶硅 (LTPS) 和氧化铟镓锌 (IGZO) TFT 开发，误差远小于 $100~\mu \text{V}$为表面电势。本研究使用校准的多变量动力学方程深入研究了 IGZO TFT 中正偏压温度不稳定性 (PBTI) 的显着老化效应。研究结果表明，这种方法有潜力通过使用先进的 LTPO 技术来增强传感和处理系统的紧凑集成（ $\sim 6~\mu \text{m}$ ）。此外，LTPO-CMOS-TFT用于实现数字电路，实现了能源效率 $3\次$高于最先进的灵活处理器。与广泛使用的伪CMOS逻辑相比，互补逻辑的能耗和速度分别提高了122.7和3.19倍。