ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai, 200433, China.
Nat Commun. 2021 Jan 4;12(1):53. doi: 10.1038/s41467-020-20257-2.
With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-InSe ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency.
随着大数据时代的到来,应用程序更加以数据为中心,由于内存和计算的物理分离,频繁的数据交互导致的能源效率问题将变得更加严重。新兴技术已经被提出,通过内存进行模拟计算来解决这一两难问题。铁电存储器由于其场驱动的快速切换和非破坏性读出而成为一种很有前途的技术,但其耐用性和小型化受到限制。在这里,我们展示了一种集成非易失性存储器和神经计算功能的α-InSe 铁电半导体沟道器件。显著的性能包括超快速的 40ns 写入速度、通过内部电场提高的耐用性、神经可塑性的灵活调节、每个事件 234/40fJ 的超低能耗用于激发/抑制、以及热调制的 94.74%高精度虹膜识别分类模拟。这个原型演示为具有高密度和高能效的集成存储计算系统奠定了基础。