Effect of Annealing Temperature for Ni/AlOx/Pt RRAM Devices Fabricated with Solution-Based Dielectric

Abstract

Resistive random access memory (RRAM) devices with Ni/AlOx/Pt-structure were manufactured by deposition of a solution-based aluminum oxide (AlOx) dielectric layer which was subsequently annealed at temperatures from 200 °C to 300 °C, in increments of 25 °C. The devices displayed typical bipolar resistive switching characteristics. Investigations were carried out on the effect of different annealing temperatures for associated RRAM devices to show that performance was correlated with changes of hydroxyl group concentration in the AlOx thin films. The annealing temperature of 250 °C was found to be optimal for the dielectric layer, exhibiting superior performance of the RRAM devices with the lowest operation voltage (<1.5 V), the highest ON/OFF ratio (>104), the narrowest resistance distribution, the longest retention time (>104 s) and the most endurance cycles (>150).

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

“…Therefore, some thin film materials with good electrical conductivity are then chosen as candidates of the electrode, such as pure metal, semiconductor and graphene materials [1][2][3]. For most bipolar RRAM devices, reactive metal like titanium (Ti) [8], nickel (Ni) [9], copper (Cu) [10] and silver (Ag) [11] are always used as TE due to their high metal activity. Compared with reactive metal, where Platinum (Pt) [12] always acts as BE in order to provide activity variation of RRAM devices.…”

“…Currently, several main fabrication technologies for RS layers have received extensive recognition by researchers, such as atomic layer deposition (ALD), magnetron sputtering, chemical vapor deposition (CVD) and solution-processed deposition. For most inorganic thin film materials used on RRAM devices (such as metal oxides and solid electrolyte), ALD and sputtering are two of the most advanced technologies due to stable performance of RS layers fabricated accordingly [5][6][7][8][9][10][11]. In addition, our previous study indicated that some other fabrication techniques for 2D thin film materials (such as graphene, GO and hexagonal boron nitride (h-BN), h-BN is considered as one of the most promising 2D materials with the function as 2D insulating template for high performance 2D electronic and photonic devices) have also been under intensive consideration, including nucleation and growth, liquid phase exfoliation and electrochemical exfoliation [21].…”

“…In this review, we will present RRAM-related inorganic materials with oxides, solid electrolyte and two-dimensional (2D) materials. [9,20,75], NiO [8,12,76], TiO 2 [21,77,78], HfO 2 [60,79,80], ZnO [81][82][83], and ZrO 2 [22,84,85] are always playing main roles in materials application of RS medium. The first report of RS performance in binary metal oxides was proposed by Hickmott in 1962 [91], which demonstrated the RS characteristics of Al/Al 2 O 3 /Al device under the effect of an electric field.…”

“…During the research and selection of different materials for electrode of RRAM devices, some vital factors should be under special consideration, such as the activity of materials, work function of materials and the interface between electrode and RS layer [48]. In general, the elementary substance metals are the most common and widely applied in RRAM devices, including Hf [129], Al [27], Ti [8], Zr [129], Cr [27], Ni [9], Cu [10], Ag [11], Pt [12], and Au [78]. Most of the time, RRAM devices always exhibit unipolar RS performance with noble metal electrode, such as Pt and Ru both as TE and BE [48,88].…”

Editorial for the Special Issue on Emerging Memory and Computing Devices in the Era of Intelligent Machines

“…(ii) There are also several examples of RRAM and phase-change memories discussed throughout the selected articles. These range from material-and cell-level studies (X. Lian et al [4]; Z. Shen et al [5]; C. Xie et al [6]; and K. Drake et al [7]), to the applications of RRAM in processing of biosignals (Y. K. Lee et al [8]), neural networks (S. Jo et al [9]; and S. N. Truong [10]), and nonvolatile processors (X. Xue et al [11]). (iii) Several of the selected articles discuss new computing paradigms that may take advantage of emerging memory devices (G. Santoro et al [12] and S. Nam et al [13]), as well as extensions, modifications, or innovations in existing volatile and nonvolatile memory technologies (at both the device and circuit levels), which may add new functionalities or improve their performance for computing applications (H. H. Shin et al [14]; S. Yang et al [15]; A. Subbiah and T. Ogunfunmi [16]; H. E. Yantir et al [17]; and L. Gan et al [18]).…”