Application of neuromorphic resistive random access memory in image processing

Jiang Bi-Yi; Zhou Fei-Chi; Chai Yang

doi:10.7498/aps.71.20220463

Abstract

With the increasing demands for processing images and videos at edge terminals, complementary metal oxide semiconductor (CMOS) hardware systems based on conventional Von Neumann architectures are facing challenges in terms of energy consumption, speed, and footprint. Neuromorphic devices, including resistive random access memory with integrated storage-computation characteristic and optoelectronic resistive random access memory with highly integrated in-sensor computing characteristic, show great potential applications in image processing due to their high similarity to biological neural systems and advantages of high energy efficiency, high integration level, and wide bandwidth. These devices can be used not only to accelerate large numbers of computational tasks in conventional image preprocessing and higher-level image processing algorithms, but also to implement highly efficient biomimetic image processing algorithms. In this paper, we first introduce the state-of-the-art neuromorphic resistive random access memory and optoelectronic neuromorphic resistive random access memory, then review the hardware implementation of and challenges to image processing based on these devices, and finally provide perspectives of their future developments.

Keywords:

neuromorphic resistive random access memory /
image preprocessing /
image recognition

Authors and contacts

1.
School of Microelectronics, Southern University of Science and Technology, Shenzhen 518000, China
2.
Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China

Corresponding author: Zhou Fei-Chi, zhoufc@sustech.edu.cn ; Chai Yang, ychai@polyu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62104091, 62174074), the Natural Science Foundation of Guangdong Province, China (Grant No. 2022A1515011064), the Guangdong Youth Innovation Talent Fund, China (Grant No. 2021KQNCX077), and NSQKJJ Fund, China (Grant Nos. K21799131, K21799128).

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图 1 氧空位导电丝型RRAM^[31]　(a) Pt/ZnO/Pt RRAM内生成的氧空位导电丝; (b) Pt/ZnO/Pt RRAM内导电丝的断裂; (c) 导电丝生成(蓝)/断裂(红)过程中器件的I-V特性曲线

Figure 1. Oxygen vacancy conductive filament in RRAM^[31]: (a) Oxygen vacancy conductive filament formed in Pt/ZnO/Pt RRAM; (b) rupture of conductive filament in Pt/ZnO/Pt RRAM; (c) I-V characteristic curves of the device during conductive filament formation (blue) and rupture (red).

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图 2 应用于ANN的神经形态RRAM阵列　(a) 1R阵列的VMM运算示意图^[36]; (b) 1T1R阵列实现ANN的方式^[40]; (c) 1S1R阵列结构^[42]

Figure 2. Neuromorphic RRAM arrays applied to ANN: (a) Schematic diagram of the VMM operation of 1R array^[36]; (b) method of implementing ANN with 1T1R array^[40]; (c) structure of 1S1R array^[42].

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图 3 Pt/KNbO₃/TiN 神经形态RRAM^[58]　(a) 具有40 μm时间差的突触前脉冲(红)和突触后脉冲(绿), 以及对应的等效输入脉冲(蓝); (b) 器件的STDP特性; (c) STP($ {I}_{2}-{I}_{1} $)和PTP($ {I}_{10}-{I}_{1} $)特性

Figure 3. Pt/KNbO₃/TiN neuromorphic RRAM^[58]: (a) Presynaptic pulse (red) and postsynaptic pulse (green) with 40 μm time difference, and the equivalent input pulse (blue) of the RRAM; (b) STDP characteristic; (c) STP ($ {I}_{2}-{I}_{1} $) and PTP ($ {I}_{10}-{I}_{1} $) characteristics.

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图 4 两端Ag/V₂C/W型RRAM器件^[68]　(a) RRAM的TS特性; (b) RRAM作为人工LIF神经元(左)和神经元的LIF行为(右); LIF人工神经元输出脉冲频率受(c)输入脉冲频率和(d) 输入脉冲幅值调控

Figure 4. Two-terminal Ag/V₂C/W type RRAM^[68]: (a) TS characteristic of RRAM; (b) RRAM as an artificial LIF neuron (left) and the corresponding LIF behavior (right); modulation of LIF artificial neuron output frequency by (c) the input pulse frequency and (d) the input pulse amplitude.

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图 5 Si/SiO₂/Si₃N₄/SiO₂/Si浮栅型神经形态RRAM^[70]　(a) 作为人工神经元; (b) 作为人工突触; (c) 人工突触和突触后神经元连接方式; (d) 人工神经元LIF行为产生的输出脉冲频率与所连接的人工突触权重大小的关系

Figure 5. Si/SiO₂/Si₃N₄/SiO₂/Si floating gate neuromorphic RRAM device^[70]: (a) As artificial neuron; (b) as artificial synapse; (c) connection of artificial synapse and postsynaptic artificial neuron; (d) effects of connected synaptic weight on the artificial LIF neuron output frequency.

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图 6 ITO/Nb-SrTiO₃/Ag结构的神经形态ORRAM^[76]　(a) 光电调控的阻变机理; (b) 通过改变输入光脉冲频率或数量实现的STP和LTP特性之间的转换; (c) 器件阵列记忆强度随输入电压幅值增加而增强的特性

Figure 6. ITO/Nb-SrTiO₃/Ag neuromorphic ORRAM^[76]: (a) Optoelectronic resistive switching mechanism; (b) transition between STP and LTP characteristics by changing the frequency or number of input optical pulses; (c) enhanced memory characteristics in the array with increased input voltage amplitude.

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图 7 ITO/MoO_x/Pd 神经形态ORRAM^[78]　(a) 器件结构; (b) 基于Mo离子价态转变的电阻调控机理; (c) ORRAM的STP特性; (d) ORRAM的LTP特性

Figure 7. ITO/MoO_x/Pd neuromorphic ORRAM^[78]: (a) Device structure; (b) resistive switching mechanism based on change of Mo ion valence state; (c) STP characteristic of ORRAM; (d) LTP characteristic of ORRAM.

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图 8 基于BN/WSe₂异质结结构的三端ORRAM^[81]　(a) 器件结构; (b) 光电调控的阻变原理; (c) ORRAM组成的阵列对不同波长光输入的不同存储效应

Figure 8. Three-terminal ORRAM device based on BN/WSe₂ heterostructure^[81]: (a) Device structure; (b) switching mechanisms; (c) different storage levels resulted from different light wavelengths in ORRAM array.

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图 9 Au/富氧IGZO/缺氧IGZO/Pt结构的ORRAM^[85]　(a) 器件结构; (b) 可见光脉冲(420 nm)使器件电导率上升和近红外光脉冲(800 nm)使器件电导率降低的过程; (c) 光调控的STDP特性

Figure 9. Au/oxygen-deficient IGZO/oxygen-rich IGZO/Pt ORRAM^[85]: (a) Device structure; (b) conductivity increasing realized by visible light pulses (420 nm) and conductivity decreasing realized by near-infrared light pulses (800 nm); (c) light modulated STDP characteristic.

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图 10 基于ReS₂ ORRAM与CMOS LIF神经元构建的光可调控神经元^[89]　(a) 光可调控神经元结构; (b) 光可调控神经元输出脉冲频率在光照下增加的行为

Figure 10. Light tunable artificial neuron based on ReS₂ ORRAM and CMOS LIF neuron ^[89]: (a) Structure of light tunable artificial neuron; (b) increasing of light tunable artificial neuron output frequency in response to light illumination.

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图 11 基于神经形态阻变器件频率差检测电路实现的图像边缘提取^[94]　(a) 基于RRAM分压器的频率差检测电路(右)和所使用的器件结构(左); (b) 两组输入脉冲频率相同(左)和不同(右)时频率差检测电路的输出; (c) 原图和频率差检测电路提取的图片边缘

Figure 11. Edge detection based on frequency difference circuit implemented by neuromorphic RRAM^[94]: (a) Frequency difference detection circuit (right) and the adopted RRAM (left); (b) output of the frequency difference detection circuit when two sets of the input pulses are at the same frequency (left) and different frequencies (right), respectively; (c) original image and extracted edges by frequency difference detection circuit.

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图 12 基于RRAM和CMOS晶体管人工视网膜单元实现的边缘提取^[99]　(a) 生物视网膜系统(光感受-双极-神经节细胞)对不同输入光照的不同输出脉冲频率; (b) 人工视网膜单元结构; (c) 人工视网膜单元输出信号V₀随V_th端口输入信号和input端口输入信号的变化

Figure 12. Unit of artificial retinal system based on RRAM for edge extraction^[99]: (a) Different output frequencies of the biological retinal system (photoreceptor cells-bipolar cells-ganglion cells) in response to different light pulse inputs; (b) structure of artificial retinal system unit; (c) change of the artificial retinal system unit output signal V₀ with respect to input signals from V_th port and input port

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图 13 基于Ag/HfO₂/C RRAM人工神经节细胞实现的运动检测^[11]　(a) 具有给光/撤光反应机制的生物视网膜系统结构; (b) 人工神经节细胞结构; (c) 人工神经节细胞工作原理; (d) 包含4个人工神经节细胞的RRAM阵列

Figure 13. Artificial ganglion cell based on Ag/HfO₂/C RRAM for motion detection^[11]: (a) Structure of biological retinal system with both excitation and inhibition response to optical input; (b) structure of artificial ganglion cells; (c) working principle of artificial ganglion cells; (d) RRAM array realized with four artificial ganglion cells.

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图 14 基于Ag/FLBP-CsPbBr₃/ITO ORRAM类眼球形阵列实现的运动检测^[105]　(a) 单个器件的结构; (b) 生物LGMD细胞输出脉冲频率对接近物体的非线性反应; (c) 基于Ag/FLBP-CsPbBr₃/ITO ORRAM实现的人工LGMD神经元对生物LGMD神经元非线性响应特性的模仿; (d) 柔性Ag/FLBP-CsPbBr₃/ITO ORRAM构建的类眼球形阵列

Figure 14. Ag/FLBP-CsPbBr₃/ITO ORRAM array based biometric compound eye for motion detection^[105]: (a) Structure of single device; (b) nonlinear response to approaching objects regarding output spike frequency of biological LGMD cell; (c) emulation of the nonlinear response properties in biological LGMD neuron by artificial LGMD neuron based on Ag/FLBP-CsPbBr₃/ITO ORRAM; (d) flexible Ag/FLBP-CsPbBr₃/ITO ORRAM array as biometric compound eye.

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图 15 基于ITO/MoO_x/Pd ORRAM阵列实现的图像锐化^[78]　(a) 8$ \times $8 ITO/MoO_x/Pd ORRAM阵列; (b) ORRAM阵列的非线性阻变特性; (c) 基于ORRAM图像锐化阵列和图像识别神经网络的人工视觉系统

Figure 15. ITO/MoO_x/Pd ORRAM array for image sharpening^[78]: (a) 8$ \times $8 ITO/MoO_x/Pd ORRAM array; (b) nonlinear resistance switching characteristics of the ORRAM array; (c) an artificial vision system based on ORRAM image sharpening array and image recognition neural network.

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Vol. 71, Issue 14 - 2022-07-20

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