Abstract：
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
摘要：
随着人工智能的发展，由于感官终端之间繁琐的数据交互，来自感官终端的数据的爆炸性增长引起了严重的能效瓶颈问题，记忆,和计算模块。诸如小芯片技术之类的异构集成方法可以显着减少不必要的数据移动；但是，它们未能解决由于计算和感觉组件的物理分离而导致的大量时间和精力开销的基本问题。受大脑启发的传感器内神经形态计算（ISNC）为此类数据密集型应用提供了足够的空间。然而,开发ISNC系统的一个关键障碍是传感器和计算单元中部署的材料系统和制造过程之间缺乏兼容性。本研究通过实现完全CMOS兼容的TiN/HfOx基神经元阵列，成功解决了这一挑战。开发的ISNC系统展示了几个有利的特点，包括多级模拟调制，最小分散，并且电导率没有显著下降(@125°C)。这些特征实现了稳定和可重复的神经形态计算。此外,该装置表现出可调节的感官和多存储存储过程。此外，该系统以93%的高准确率实现了信息识别，以及频率选择性和显著的活动依赖性可塑性。这项工作为负担得起且高效的感觉神经形态系统提供了有希望的途径。
