Cross-Point Synapse Array Fabricated with HfO2 Based Memristor-Cells and Super-Linear Threshold Selectors and HfO2 Based Integrate-and-Fire Neurons
Authors: Kim Heajee , Kwon Ki-Hyun , Kim Dong Won , Jin Soo-Min , Woo Dae-Seong , Park Sang-Hong , Park Jeagun 
Published: December 2020

Recently, a neuromorphic-chip performing artificial intelligence (AI) has been intensively for the application field of pattern recognition, autonomous car, etc. Biological neurons, which is able to be described hardware-wise by a cross-point synapse array being connected with input and output neurons like a vector multiplying arithmetic operation. The conventional neuromorphic-chip had been developed by a cross-point synapse array using C-MOSFET integrated circuit, where a synapse was realized by SRAMs while a neuron was achieved by an integration of C-MOSFETs and capacitors [1]. Thus, it presented a detrimental fault such as a large synapse and neuron size. As a solution, a cross-point synapse array using memristors has been researched popularly[2, 3]. However, it produced a sneak current during potentiating or depressing a selected memristor-synapse. To eliminate a sneak current in a memristor-based-synapse array, a n-MOSFET being connected with a synapse cell has proposed as a selector, but it has presented three terminals operating simultaneously a synapse-cell and a selector. Hence, recently, it has been intensively researched that a selector (S) is stacked vertically with a memristor-cell (M), called 1S1M-based synapse array, as shown in Fig. 1(a).

In our study, a synapse array was fabricated with the HfO2 based memristor-cells being vertically stacked with super-linear threshold selectors, as shown in Fig. 1(b). The HfO2 based memristor showed 3-bit resistances for potentiation and depression, being adjusted by a reset voltage in the negative-resistance-region (NDR) of the memristor synapse, as shown in Fig. 1 (c). The potentiation and depression nature such as linearity and symmetry was estimated with the spike width of 100 μs and input spike number of 100, showing a good linearity (i.e., 3.27 for potentiation and -6.14 for depression) and symmetry nature (i.e., 0.12 ), as shown in Fig. 1(d). The memristor-synapse nature of 1S1M presented 3-bit resistances for potentiation and depression, the dead region between ~-0.40 and ~+0.42 V, the switching-on threshold voltages of ~-0.40 for negative applied bias and ~+0.42 V for positive applied bias, the set voltage of 0.92 V, and the reset voltage of -1.05 V, as shown in Fig. 1(e). This result well demonstrated a cross-point memristor-synapse array being able to operate a half-bias or 1/3-bias scheme writing.

In addition, the effect of our proposed neural network on the pattern recognition accuracy was estimated by simulation. The cross-point neural network without a super-linear selector (i.e., 1M array), as shown in Fig. 1(f), was compared by that with a super-linear selector (i.e., 1S1M array), as shown in Fig. 1(g). Our proposed neuron was designed with a HfO2 based neuron having an integrate nature and a sense amplifier using 7 n-MOSFETs and 3 p-MOSFETs, as shown in Fig. 1(h). The neural network fabricated with 1S1M array and HfO2 based neurons showed ~10 % than that fabricated with 1M array and HfO2 based neurons, as shown in Fig. 1(i). This result indicates that the implementation of a super-linear threshold selector being vertically stacked on a memristor-synapse would be essentially necessary for a high accurate AI application.

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2016M3A7B4910249).

This material is based upon work supported by the Ministry of Trade, Industry & Energy(MOTIE, Korea) under Industrial Technology Innovation Program (10068055).

Reference

[1] Merolla, Paul A., et al. "A million spiking-neuron integrated circuit with a scalable communication network and interface." Science 345.6197 (2014): 668-673.

[2] Wu, Chaoxing, et al. "Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability." Nature communications 8.1 (2017): 1-9.

[3] Moon, K., et al. "RRAM-based synapse devices for neuromorphic systems." Faraday discussions 213 (2019): 421-451.

Figure 1