State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China.
Mater Horiz. 2021 Nov 29;8(12):3345-3355. doi: 10.1039/d1mh01257f.
In-memory logic calculations and brain-inspired artificial synaptic neuromorphic computing are expected to solve the limitations of the traditional von Neumann computing architecture. The data processing efficiency of the traditional von Neumann architecture is inherently limited by its physically separated processing and storage units, and thus data transmission besides calculation leads to a limited calculation speed and additional high-power consumption. In addition, traditional digital logic calculations and analog calculations have greater limitations in conversion. Herein, we report a flexible two-terminal memristor based on SiCO:H, which is a porous low- back-end complementary metal-oxide-semiconductor (CMOS)-compatible material. Due to its low operating voltage (200 mV) and fast response speed (100 ns), it could perform digital memory calculation and neuromorphic calculation simultaneously. The memristor could realize a transition from short-term to long-term plasticity in the process of enhancement and inhibition during neuromorphic calculation, with high biological reality. In digital logic calculations, IMP-based and MAGIC-based logic calculations were verified. In neuromorphic computing, an Ag ion-based conductive filament was introduced. The relationship between the temporal dynamics of the conductance evolution and the diffusive dynamics of the Ag active metal could be modulated by the external programming electric field strength. The synapses and neuron dynamics in biology were faithfully simulated, realizing a transition from short-term to long-term plasticity in the process of enhancement and inhibition, which has high compatibility and scalability, proposing a novel solution for the next generation of computer architectures.
在内存中进行逻辑计算和受大脑启发的人工突触神经形态计算有望解决传统冯·诺依曼计算体系结构的局限性。传统冯·诺依曼体系结构的数据处理效率受到其物理上分离的处理和存储单元的固有限制，因此除了计算之外的数据传输导致计算速度有限并且额外消耗高功率。此外，传统的数字逻辑计算和模拟计算在转换方面具有更大的局限性。在此，我们报告了一种基于 SiCO:H 的灵活的两端忆阻器，这是一种多孔的低后端互补金属氧化物半导体 (CMOS) 兼容材料。由于其低工作电压 (200 mV) 和快速响应速度 (100 ns)，它可以同时进行数字存储计算和神经形态计算。忆阻器可以在神经形态计算过程中的增强和抑制过程中实现从短期到长期可塑性的转变，具有较高的生物学真实性。在数字逻辑计算中，验证了基于 IMP 和 MAGIC 的逻辑计算。在神经形态计算中，引入了 Ag 离子基导电线。外部编程电场强度可以调节电导演化的时间动力学与 Ag 活性金属的扩散动力学之间的关系。生物中的突触和神经元动力学被忠实地模拟，在增强和抑制过程中实现从短期到长期可塑性的转变，具有较高的兼容性和可扩展性，为下一代计算机体系结构提出了一种新的解决方案。
