HfO _x -based nano-wedge structured resistive switching memory device operating at sub-μA current for neuromorphic computing application

Abstract

We fabricated a silicon based nano-wedge resistive switching memory device with the stack of Ti/HfO x /p + -Si. By using 25% tetra-methyl-ammonium hydroxide (TMAH) aqueous solution, the anisotropic wet etching process is carried out to minimize the tip structure of the silicon bottom electrode to a width of 4 nm, and the structure was validated through TEM analysis. Due to the minimized device area, low read current levels (<1 μA) were obtained in the nano-wedge RRAM while the opposites were measured in large size RRAM devices. In addition, the fabricated nano-wedge RRAM exhibited low power consumption during the DC switching process. Additionally, pulse measurement and retention tests were performed to demonstrate the synaptic behaviors of long-term potentiation and depression. Software neural network simulation was followed to test the feasibility of nano-wedge RRAM's array implementation. These results demonstrate the fabricated nano-wedge RRAM devices' potential usage as a synaptic device in neuromorphic computing systems.

Core-Shell Dual-Gate Nanowire Charge-Trap Memory for Synaptic Operations for Neuromorphic Applications

“…The brain functions (observation, reorganization, learning, and memorization) are performed by neurons (computing elements) and synapses (memory elements) [1,2]. In the neuromorphic system, an artificial synaptic device plays a key role in linking the artificial neurons and modulating the connection strength (synaptic weight) between neurons [3][4][5][6][7][8][9][10][11][12][13][14][15]. In order to realize brain-like computing, different types of artificial synaptic devices have been proposed for artificial intelligence applications [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22].…”

“…In the neuromorphic system, an artificial synaptic device plays a key role in linking the artificial neurons and modulating the connection strength (synaptic weight) between neurons [3][4][5][6][7][8][9][10][11][12][13][14][15]. In order to realize brain-like computing, different types of artificial synaptic devices have been proposed for artificial intelligence applications [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The major applications for these artificial synaptic transistors are neuromorphic in-memory computing chip, artificial sensory perception, humanoid robotics, memorize, and recognize massive and unstructured data through parallel and power-efficient ways [3][4][5][6][7][8][9][10][11][12][13][14][15]…”

“…In order to realize brain-like computing, different types of artificial synaptic devices have been proposed for artificial intelligence applications [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The major applications for these artificial synaptic transistors are neuromorphic in-memory computing chip, artificial sensory perception, humanoid robotics, memorize, and recognize massive and unstructured data through parallel and power-efficient ways [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Charge tapping/de-trapping based artificial synapse are favorable for in-memory computing applications due to their stable analogue conductance state and nonvolatile characteristic [12].…”

Synaptic Characteristics of Amorphous Boron Nitride-Based Memristors on a Highly Doped Silicon Substrate for Neuromorphic Engineering

“…Hexagonal boron nitride (h-BN) is one of the 2D materials whose good resistive switching properties at the grain boundaries have already been verified. , In this study, the resistive and synaptic properties of a memristor are demonstrated using amorphous boron nitride ( a -BN) deposited by applying radio frequency (RF) magnetron sputtering on a highly doped silicon substrate that is more suitable for the complementary metal-oxide semiconductor (CMOS) process than other substrates. It should be noted that by adjusting the doping concentration of the Si bottom electrode, the linear and rectifying properties can be obtained, and a scaled device can be obtained through anisotropic wet etching of silicon. , …”

“…It should be noted that by adjusting the doping concentration of the Si bottom electrode, the linear and rectifying properties can be obtained, 38 and a scaled device can be obtained through anisotropic wet etching of silicon. 39,40 a-BN is an insulator with a band gap of 3.8−5.05 eV, dielectric constant of 5−6, and high dielectric strength of ∼9 MV/cm. 41−43 Stoichiometric thin films are transparent and insulating; hence, they can be used to form dielectric layers in electronic devices.…”