IEEE Trans Neural Netw Learn Syst. 2015 Oct;26(10):2596-601. doi: 10.1109/TNNLS.2015.2388633. Epub 2015 Feb 5.
This brief describes the neuromorphic very large scale integration implementation of a synapse utilizing a single floating-gate (FG) transistor that can be used to store a weight in a nonvolatile manner and demonstrate biological learning rules such as spike-timing-dependent plasticity (STDP). The experimental STDP plot (change in weight against ∆t=tpost - tpre ) of a traditional FG synapse from previous studies shows a depression instead of potentiation at some range of positive values of ∆t -we call this non-STDP behavior. In this brief, we first analyze theoretically the reason for this anomaly and then present a simple solution based on changing control gate waveforms of the FG device to make the weight change conform closely to biological observations over a wide range of parameters. The experimental results from an FG synapse fabricated in AMS 0.35- μ m CMOS process design are also presented to justify the claim. Finally, we present the simulation results of a circuit designed to create the modified gate voltage waveform.
本简要描述了一种利用单个浮栅 (FG) 晶体管实现的神经形态超大规模集成突触,该晶体管可以以非易失方式存储权重,并演示生物学习规则,如尖峰时间依赖性可塑性 (STDP)。来自先前研究的传统 FG 突触的实验性 STDP 图(权重变化与 ∆t=tpost - tpre 之间的关系)显示,在 ∆t 的某些正值范围内出现抑制而不是增强 - 我们称这种行为为非 STDP 行为。在本简要中,我们首先从理论上分析了这种异常的原因,然后提出了一种简单的解决方案,基于改变 FG 器件的控制栅极波形,使权重变化在广泛的参数范围内紧密符合生物观察。还提出了在 AMS 0.35- μ m CMOS 工艺设计中制造的 FG 突触的实验结果,以证明这一说法。最后,我们提出了一个设计用于创建修改后的栅极电压波形的电路的仿真结果。