Supporting Information

Multi-Spectral Gate-Triggered Heterogeneous Photonic Neuro-transistors for Power-Efficient Brain-Inspired Neuromorphic Computing

Sung Woon Choa, Sung Min Kwona, Minkyung Leeb, Jeong-Wan Joa, Jae Sang Heoc, Yong-Hoon Kimb,d,*, Hyung Koun Chod,*, and Sung Kyu Parka,*

aSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974,

Republic of Korea

bSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon

16419, Republic of Korea

cDepartment of Medicine, University of Connecticut School of Medicine, Farmington, CT

06030, USA

dSchool of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon

16419, Republic of Korea

*Corresponding Authors: skpark@cau.ac.kr (S. K. Park), yhkim76@skku.edu (Y.-H. Kim),

chohk@skku.edu (H. K. Cho)

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Fig. S1. Hysteresis curves and charge-trap state density of transistors with ZTO single-layer

and pristine/post-annealed CdS/ZTO heterogeneous channel structures; (a) ZTO single-layer,

(b) CdS/ZTO, (c) post-annealed CdS/ZTO (250 ºC), and (d) post-annealed CdS/ZTO (400

ºC).

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Fig. S2. The EPSC peaks triggered on transistors by three types of optical inputs (UV,

blue, and green-light spikes); (a) CdS/ZTO, (b) post-annealed CdS/ZTO, and (c) ZTO.

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Fig. S3. The uniform performance and mean decay time (τdec) of EPSCs triggered on

CdS/ZTO neuro-transistor by three different types of optical spikes (UV, blue, and

green). Here, the τdec value was extracted from the exponential decay curve of the

normalized PSC vs. time.

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Fig. S4. The EPSC variation of CdS/ZTO heterogeneous neuro-transistor depending on

green-light spike conditions; (a) optical power density (writing power-consumption, WP

= power densitiy × channel area) and (b) optical spike duration.

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Fig. S5. (a-c) Spike-rate/-number dependent plasticity behaviors of EPSC peaks triggered

from multiple green-light inputs; (a) overall variation of EPSC peaks, (b) EPSC gain, and

(c) STP/LTP transistion trend. (d) Spike-rate/-number dependent plasticity behaviors

triggered from high-frequency multiple spikes of green, blue, and UV.

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Fig. S6. PPF index by paired green-light spikes and their variation by modulatory input

(VM); (a) the summary of VM-dependent PFF index and (b) the EPSC curves triggered by

paired green-light spikes in various VM conditions.

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Fig. S7. VM-dependent LTP and STP dynamics of heterogeneous photonic neuro-

transistor triggered by multiple green-light inputs with different spiking frequency; (a)

LTP and (b) STP dynamics.

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Fig. S8. The gain generation mechanism of second peak on spatio-temporally integrated

EPSC.

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Fig. S9. All-optical inputs driven Pavlovian associative learning process mimicked from

MC/MO heterogeneous neuromorphic transistor with neuronal compution processing

performance of multispectral optical information; (a) UV input (US, feeding food) before

conditioning, (b) green input (NS, bell ringing) before conditioning, (c) conditioning;

spatial integration by simultaneous binary optical inputs (UV + Green) (d) green input

(CS, bell ringing) after conditioning; spatio-temporal integration by non-simultaneous

binary optical inputs (UV→ G), and (e) green input (NS, bell ringing) after extinction.

Here, the threshold for action firing of POSTN response (sallivation) is setted to 1 μA.

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Fig. S10. All-optical inputs driven Pavlovian dog associative learning modeling including

“transient reawakening” step mimicked from MC/MO heterogeneous neuromorphic transistor;

Here, the louder bell ringing input was performed via green-mode input with higher optical

power-density (P = 20 mW/cm2).

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Table S1. Summary of figure-of-merits of current electric- and photonic-type

neuromorphic transistors.

Type Materials(Oxide based)

Channel area (μm2)[device size]

On-switching speed (ms)

Writing input spike mode

Synaptic function

NeuronalComputation function

Advanced application

Refs.

Electric ZnO/Ion-gel(Channel/electrolyte)

2.5 × 104 10 ~ 2,000 Electrical biasEPSC/IPSC, STDP,STP/LTP

N.A. N.A. [S1]

IZO/H;SiO2 8.0 × 104 10 Electrical bias EPSC, STP/LTP Dendritic sum. N.A. [S2]

IZO/GO 8.0 × 104 10 Electrical bias EPSCLogic process.Dendritic sum.

N.A. [S3]

ITO/Chitosan 8.0 × 104 10 ~ 9,000 Electrical biasEPSC/IPSC STP/LTP,STDP

N.A.Association learning

[S4]

ITO/Starch 8.0 × 104 10 ~ 100 Electrical bias EPSC Dendritic sum. N.A. [S5]

IWO/Ionic liquid

1.5 × 105 20~500 Electrical bias PPF, STDP N.A. N.A. [S6]

Photonic IZO(single channel)

5.0 × 104 500Single-spectrum (UV)

EPSC, SFDP, SDDP, SRDP

N.A. N.A. [S7]

IGZO(single channel)

8.0 × 104 20Single-spectrum (UV)

EPSC, SRDP, PPF, STP/LTP

N.A. N.A. [S8]

IGZO(single channel)

8.0 × 103 100Single-spectrum (UV)

EPSC/IPSC PPF/PPD, SRDP

N.A.Pattern recognition

[S9]

IGZO(single channel)

4.0 × 103 1,000Single-spectrum (UV)

EPSC/IPSC PPF/PPD, SRDP, STP/LTP

N.A. N.A. [S10]

IGZO(single channel)

1.0 × 104 50Single-spectrum (UV)

EPSC/IPSC PPF, SRDP

N.A.Pattern recognition

[S11]

CdS/ZTO(heterogeneous structure)

2.5 × 104 500Multi-spectrum(UV, B, G)

EPSC, SRDP, PPF, STP/LTP

Dendritic sum.Logic process.

Association learning

This work

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