[paper] artificial synapse

Md. Hasan Raza Ansari, Udaya Mohanan Kannan, and Nazek El-Atab

Silicon Nanowire Charge Trapping Memory for Energy-Efficient Neuromorphic Computing

IEEE Transactions on Nanotechnology (2023)

DOI 10.1109/TNANO.2023.3296673

SAMA Labs, CEMSE Division, KAUST, Thuwal 23955-6900, Saudi Arabia

Department of Electronic Engineering, Gachon University, Seongnam 13120, Korea

Abstract: This work highlights the utilization of the floating body effect and charge-trapping/de-trapping phenomenon of a Silicon-nanowire (Si-nanowire) charge-trapping memory for an artificial synapse of neuromorphic computing application. Charge trapping/de-trapping in the nitride layer characterizes the long-term potentiation (LTP)/depression (LTD). The accumulation of holes in the potential well achieves short-term potentiation (STP) and controls the transition from STP to LTP. Also, the transition from STP to LTP is analyzed through gate length scaling and high-κ material (Al2O3) for blocking oxide. Furthermore, the conductance values of the device are utilized for system-level simulation. System-level hardware parameters of a convolutional neural network (CNN) for inference applications are evaluated and compared to a static random-access memory (SRAM) device and charge-trapping memory. The results confirm that the Si-nanowire transistor with better gate controllability has a high retention time for LTP states, consumes low power, and archives better accuracy (91.27%). These results make the device suitable for low-power neuromorphic applications.

FIG: Schematic representation of biological and Si-nanowire charge trapping memory as an artificial synapse

Memory for Synaptic Operations

Md. Hasan Raza Ansari, Udaya Mohanan Kannan and Seongjae Cho 

Core-Shell Dual-Gate Nanowire Charge-Trap Memory

for Synaptic Operations for Neuromorphic Applications

Nanomaterials 2021, 11, 1773

DOI 10.3390/nano11071773

 
Graduate School of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea;
 

Abstract: This work showcases the physical insights of a core-shell dual-gate (CSDG) nanowire transistor as an artificial synaptic device with short/long-term potentiation and long-term depression (LTD) operation. Short-term potentiation (STP) is a temporary potentiation of a neural network, and it can be transformed into long-term potentiation (LTP) through repetitive stimulus. In this work, floating body effects and charge trapping are utilized to show the transition from STP to LTP while de-trapping the holes from the nitride layer shows the LTD operation. Furthermore, linearity and symmetry in conductance are achieved through optimal device design and biases. In a system-level simulation, with CSDG nanowire transistor a recognition accuracy of up to 92.28% is obtained in the Modified National Institute of Standards and Technology (MNIST) pattern recognition task. Complementary metal-oxide-semiconductor (CMOS) compatibility and high recognition accuracy makes the CSDG nanowire transistor a promising candidate for the implementation of neuromorphic hardware.

Fig: Schematic representation of biological synapse and 2D representation of CSDG nanowire transistor for artificial synapse device.

Acknowledgement: This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (No. 2016M3A7B4910348, Nano-Material Technology Development Program, 50%) and was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01294, Development of IoT based edge computing ultra-low power artificial intelligent processor, 50%).

[see also] M. H. R. Ansari, S. Cho, J.-H. Lee, and B.-G. Park, “Core-Shell Dual-Gate Nanowire Memory as a Synaptic Device for Neuromorphic Application,” IEEE Journal of the Electron Devices Society, pp. 1–1, 2021. DOI: 10.1109/JEDS.2021.3111343