Research ArticleAPPLIED SCIENCES AND ENGINEERING

One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

See allHide authors and affiliations

Science Advances  10 Jul 2020:
Vol. 6, no. 28, eaba1178
DOI: 10.1126/sciadv.aba1178
  • Fig. 1 Fabrication process of the 1D organic multi-synapses and neural signal transmission.

    (A) Fabrication process and schematics of the 1D organic artificial multi-synapses. (B) Cross-sectional and top-surface (bottom left inset) SEM images of the completed 1D synaptic device. (C) Optical photograph of the completed 1D synaptic device (right) and the coiled form on a glass tube with a diameter of 1.2 mm (left). (D) Schematic of the biological multipolar neurons. The strength of the postsynaptic response passing through each synapse is determined by the degree of w. Photo credit: Tae-Wook Kim, Jeonbuk National University.

  • Fig. 2 Synaptic characteristics and switching mechanism of the 1D organic multi-synapses.

    (A) IPSC responses triggered by different potentiating pulses of VG = −30 V for PW = 100 and 500 ms at VDS = −10 V. The right schematic presents the cross section of the 1D synaptic device and the postsynaptic responses generated by different degrees of downward P in the P(VDF-TrFE) layer according to the width of VG. (B) IPSC responses triggered by 20 repeated potentiating pulses of VG = −30 V for 300 ms with Δt = 0.5, 1.5, and 8 s, respectively (left). Retention tests after the 20 potentiating pulses with different Δt (middle). The right schematic presents the postsynaptic responses generated by different degrees of downward P in the P(VDF-TrFE) layer for Δt = 0.5 s and 8 s, respectively. (C) LTP and LTD of IPSC with different pulse amplitudes of VG ranging from ±20 to ±40 V. The number of potentiating pulses and depressing pulses is 80. VDS and PW are set as −10 V and 500 ms, respectively. The right schematic presents the postsynaptic responses generated by different degrees of P in the P(VDF-TrFE) layer for different pulse amplitudes (VG = ±20 and ± 40 V) at a fixed PW (500 ms). (D) Plot of the NL and dynamic range (inset) with respect to VG (ranging from ±20 to ±40 V). (E) Repetitive transitions of the LTP/LTD of IPSC for the 1D artificial multi-synapses during 100 cycles. (F) Red and blue circles represent the first and last three cycles, respectively, for the LTP/LTD of IPSC in (E) (marked as red and blue boxes, respectively). (G) Schematic of one bending cycle with R = 2.5 mm. (H to J) Repetitive transitions of the LTP/LTD of IPSC at different fixed bending radii (R = ∞, 5, and 2.5 mm, respectively) after 100 bending cycles. Photo credit: Seonggil Ham, Korea University.

  • Fig. 3 NOR-type textile array consisting of 1D multi-synapses.

    (A) Schematics of the signal transmission in a biological neural network (top) and the circuit diagram of NOR-type 2 by 2 array (bottom), representing the integrated postsynaptic response generated by two presynaptic pulses with different timings. (B) Plot of the integrated IPSC in the 2 by 2 array with alternately applied potentiating VG1 and VG2 pulses (−30 V for 500 ms) with Δt = 5 s at each preneuron. The inset is a photograph of the 2 by 2 array. (C) Plot of the integrated IPSC in the NOR-type 3 by 2 array with respect to the learning phases from 1 to 8. The top table shows the learning phases that are determined by a combination of the completed LTP and LTD states (by the VG sweep) of the cells (A, B, and C cells). The inset is a photograph of the 3 by 2 array. Note that each integrated IPSC was statistically obtained from three different 3 by 2 arrays. (D) A photograph for the 10 by 12 array on a glass substrate where it is placed inside the probe station (top). The inset shows a magnified photograph for 6 by 2 array. Schematic of the 10 by 12 array structure on a glass substrate containing 60 synaptic cells using 10 1D devices and 12 Ag wires (bottom). (E) Overlay plots of IDS-VG curves of the 60 synaptic cells at VDS = −10 V. (F) Statistical histograms of IDS for the completed LTP (ON) and LTD (OFF) states and the dynamic range for the 60 cells. (G) Encoded pattern of k in the 10 by 12 array matrix (left). The blue and light blue regions in the array represent the completed LTP and LTD states, respectively. The right graph shows six IPSC values integrated from 1 to 6 rows in the array. Photo credits: (B and C) Seonggil Ham, Korea University; (D) Tae-Wook Kim, Jeonbuk National University.

  • Fig. 4 Recognition simulation for MNIST and ECG patterns.

    (A) Recognition accuracy for MNIST patterns with respect to the number of learning epochs. The left inset shows a reshaped 28 by 28 contour image of the digit 3 from w before and after 10 epochs, and the right inset shows the confusion matrix for a classification test involving 10,000 MNIST images after 10 epochs. (B) Schematics of the five classes of ECG waveforms: N, S, V, F and Q. (C) Constituents of a single-layer neural network for the N ECG pattern recognition process in which 187 input neurons and 5 output neurons are fully connected by 935 artificial synapses. (D) Changes in the w values of 187 artificial synapses connected to the output neuron corresponding to the N class during 3200 learning phases. (E) Changed w values from all ECG classes after 50 learning epochs. (F) Recognition accuracy for the ECG patterns during 50 learning epochs in the initial state and after 100 bending cycles with different fixed bending radii (R = ∞, 5, and 2.5 mm). (G) Confusion matrices for a classification test of the 800 ECG patterns in the initial state and after 100 bending cycles with different fixed bending radii (R = ∞, 5, and 2.5 mm).

Supplementary Materials

Stay Connected to Science Advances

Navigate This Article