Research ArticleMATERIALS SCIENCE

Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice

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Science Advances  12 Feb 2020:
Vol. 6, no. 7, eaay5225
DOI: 10.1126/sciadv.aay5225
  • Fig. 1 G-PQD superstructure.

    (A) Schematic showing the growth of PQDs on graphene to form the G-PQD superstructure and the proposed applications. (B) TEM image of PQDs grown on a single layer of graphene sheets. (C) TEM image of the PQDs distributed on the G-PQD superstructure. (D) High-resolution TEM (HRTEM) image of the PQDs grown on graphene. Inset shows the corresponding FFT image. (E) HRTEM image of stress-induced changes in the graphene lattice due to the growth of PQDs (red arrow indicates distortion). (F) XRD spectra of pristine PQDs (red) and G-PQDs (blue) grown on silicon [inset: enlarged region; units remain the same, 3.3°, 4.4°, 6.5°, 9.0°, and 15.4° corresponding to (011), (101), (201), (141), and (100) crystal planes, respectively]. a.u., arbitrary units. (G) Raman spectra of pristine graphene (black), PQDs drop casted on graphene (gray), and PQDs grown on graphene (blue). CCD, charge-coupled device.

  • Fig. 2 UV-vis and PL spectra.

    (A) Ultraviolet-visible (UV-vis) absorption (red) and PL spectra (blue) of the G-PQD superstructure film. (B) PL decay profiles of PQD (red) and G-PQD films (green).

  • Fig. 3 G-PQD phototransistor.

    (A) Drain current (IDS) versus drain voltage (VDS) characteristic of the phototransistor under the dark and illumination intensity of 440 nm monochromatic light with zero gate voltage. Inset: Schematic of G-PQD superstructure phototransistor. (B) Spectral responsivity of G-PQD superstructure phototransistor. Inset: Detectivity and EQE of phototransistor under different wavelengths. Energy level diagram of the G-PQD superstructure under (C) photoexcitation and (D) photogating. VB and CB represent the valence band and conduction band of the PQDs. (E) Resistance as a function of back-gate voltage (VBG) under different illumination intensities at a given drain-source voltage VDS of 500 mV. (F) Two-dimensional plot of superstructure resistance as a function of optical power. (G) Shift of Dirac point as a function of incident light intensity. Inset: Variation of photocurrent under different illumination powers at 437 nm.

  • Fig. 4 COMSOL simulation and transient photoresponse.

    (A) Schematic of COMSOL simulation of PQDs of size 3 nm grown on a graphene film. (B) Simulated photocurrent versus input power. (C) Transient photoresponse under light illumination on and off conditions. (D) Normalized photocurrent response to on and off illumination.

  • Fig. 5 Photonic synapse performance and facial recognition.

    (A) Anatomy of two interconnected human neurons via a synapse (red box). (B) Schematic representation of biological synapses. (C) Transient characteristic of the device (VD = 0.5 V and VG = 10 V) showing the change in conductance due to a single pulse of light of pulse width 30 s for varying light intensity. (D) PPF index of the device (VD = 0.5 V and VG = 10 V) due to varying off time between two consecutive light pulses having on time of 5 s. (E) Transient characteristic of the device (VD = 0.5 V and VG = 10 V) showing the change in conductance due to varying number of light pulses having on and off time of 5 and 5 s, respectively. (F) Retention of the long-term potentiated device (VD = 0.5 V and VG = 10 V) for 3 × 103 s after application of 20 optical pulses (on and off time of 5 and 5 s, respectively). (G) Nonvolatile synaptic plasticity of the device (VG = 10 V) showing LTP by train of optical pulses (on and off time of 5 and 5 s, respectively) at VD = 0.5 V and LTD by a train of electrical pulses (−0.5 V, on and off time of 1 and 1 s, respectively) at VD. (H) Gate-dependent transient characteristic of the device (VD = 0.5 V) after application of 20 optical pulses (on and off time of 5 and 5 s, respectively).(I), Neuron network structure for face recognition. Photo credit: Sreekanth Varma and Basudev Pradhan, UCF. (J) Real images (top) for training and the synaptic weights of certain corresponding output neurons (bottom). Photo credit (from left to right): Sreekanth Varma and Basudev Pradhan, UCF; Avra Kundu and Basudev Pradhan, UCF; Basudev Pradhan, UCF; and Basudev Pradhan, UCF.

  • Table 1 Performance summary of previously reported graphene-QD–based phototransistor (MA: CH3NH3+, FA: NH2CH = CH+).

    MOF, metal-organic framework.

    Active materialsR (A W−1)D* (Jones)EQE (%)λ (nm)Thickness (nm)Reference
    FAPbBr3-graphene1.15 × 1053.42 × 107520(6)
    2D perovskite-
    graphene
    1 × 105532125(7)
    MAPbI3−xClx-CNT1 × 1043.7 × 1014400(37)
    MAPbBr2I-grapehene6 × 105250(38)
    PbS QD-graphene2.6 × 1045.5 × 1012637(39)
    MOF-graphene1.25 × 1066.9 × 10145 × 108325140(40)
    CsPbBr3-graphene
    nanoribbon
    8007.5 × 10145 × 10551230(41)
    PbS QD-MoS26 × 1055 × 101198040–60(42)
    PbS QD-graphene5 × 1077 × 1013600100(8)
    MAPbBr3 film–
    graphene
    1801× 1095 × 104400–800>100(31)
    MAPbBr3 PDs grown
    from graphene lattice
    1.4 × 1084.72 × 10154.08 × 1010430–440<20This work

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/7/eaay5225/DC1

    Supplementary Text

    Section S1. Mobility calculation

    Section S2. Pattern recognition

    Fig. S1. PQD growth mechanism on single-layer graphene.

    Fig. S2. XPS core level spectra of pristine PQDs.

    Fig. S3. XPS core level spectra of G-PQDs.

    Fig. S4. PQDs grown from graphene surface.

    Fig. S5. Graphene FET.

    Fig. S6. Shift of Dirac point due to PQDs grown on graphene.

    Fig. S7. Transient photocurrent response.

    Fig. S8. Calculation of PPF from the transient characteristic of the device for two consecutive light pulses.

    Fig. S9. STP to LTP.

    Fig. S10. Strategy to get testing dataset, which should be different from the images in the training dataset.

    Fig. S11. Synaptic weights of each output neurons from the training of MNIST dataset.

    Fig. S12. Fitted conductance change with pulse number of synapse.

    Table S1. Comparison of our work with previously reported works in the literature in terms of energy consumption.

    Table S2. Fitting parameters for potentiation and depression.

    References (4348)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Text
    • Section S1. Mobility calculation
    • Section S2. Pattern recognition
    • Fig. S1. PQD growth mechanism on single-layer graphene.
    • Fig. S2. XPS core level spectra of pristine PQDs.
    • Fig. S3. XPS core level spectra of G-PQDs.
    • Fig. S4. PQDs grown from graphene surface.
    • Fig. S5. Graphene FET.
    • Fig. S6. Shift of Dirac point due to PQDs grown on graphene.
    • Fig. S7. Transient photocurrent response.
    • Fig. S8. Calculation of PPF from the transient characteristic of the device for two consecutive light pulses.
    • Fig. S9. STP to LTP.
    • Fig. S10. Strategy to get testing dataset, which should be different from the images in the training dataset.
    • Fig. S11. Synaptic weights of each output neurons from the training of MNIST dataset.
    • Fig. S12. Fitted conductance change with pulse number of synapse.
    • Table S1. Comparison of our work with previously reported works in the literature in terms of energy consumption.
    • Table S2. Fitting parameters for potentiation and depression.
    • References (4348)

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