Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.

翻译：近年来，存储器技术、器件和材料领域的进展显示出将其集成到神经形态电子系统中的巨大潜力。然而，在这些材料的开发与实现大规模、全功能系统之间仍存在显著差距。一个关键挑战在于确定哪些器件和材料最适合特定功能，以及如何将它们与CMOS电路配对。为此，我们提出了TEXEL，一种混合信号神经形态架构，旨在探索片上学习电路与新型二端和三端器件的集成。TEXEL作为一个易用的平台，旨在弥合基于CMOS的神经形态计算与新兴器件最新进展之间的鸿沟。本文通过全面的芯片测量与仿真，证明了TEXEL已具备器件集成的条件。TEXEL为在新兴器件上测试受生物启发的学习算法提供了一个实用系统，从而在类脑计算与前沿器件研究之间建立了切实的联系。