Ming-Jinn Tsai

Bio: Ming-Jinn Tsai is an academic researcher from Industrial Technology Research Institute. The author has contributed to research in topics: Resistive random-access memory & Non-volatile memory. The author has an hindex of 40, co-authored 242 publications receiving 8411 citations. Previous affiliations of Ming-Jinn Tsai include Minghsin University of Science and Technology & National Tsing Hua University.

Metal–Oxide RRAM

Abstract: In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.

Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM

Abstract: A novel HfO2-based resistive memory with the TiN electrodes is proposed and fully integrated with 0.18 mum CMOS technology. By using a thin Ti layer as the reactive buffer layer into the anodic side of capacitor-like memory cell, excellent memory performances, such as low operation current (down to 25 muA), high on/off resistance ratio (above 1,000), fast switching speed (5 ns), satisfactory switching endurance (>106 cycles), and reliable data retention (10 years extrapolation at 200degC) have been demonstrated in our memory device. Moreover, the benefits of high yield, robust memory performance at high temperature (200degC), excellent scalability, and multi-level operation promise its application in the next generation nonvolatile memory.

Unipolar resistive switching characteristics of ZnO thin films for nonvolatile memory applications

Abstract: Highly (002)-oriented and columnar-grained ZnO thin films were prepared by radio frequency magnetron sputtering at room temperature The Pt∕ZnO∕Pt devices exhibit reversible and steady bistable resistance switching behaviors with a narrow dispersion of the resistance states and switching voltage Only a low forming electric field was required to induce the resistive switching characteristics The resistance ratios of high resistance state to low resistance state were in the range of 3–4 orders of magnitude within 100cycles of test It was also found that the conduction mechanisms dominating the low and high resistance states are Ohmic behavior and Poole-Frenkel emission, respectively

Evidence and solution of over-RESET problem for HfO X based resistive memory with sub-ns switching speed and high endurance

Abstract: The memory performances of the HfO X based bipolar resistive memory, including switching speed and memory reliability, are greatly improved in this work. Record high switching speed down to 300 ps is achieved. The cycling test shed a clear light on the wearing behavior of resistance states, and the correlation between over-RESET phenomenon and the worn low resistance state in the devices is discussed. The modified bottom electrode is proposed for the memory device to maintain the memory window and to endure resistive switching up to 1010 cycles.

A 4Mb embedded SLC resistive-RAM macro with 7.2ns read-write random-access time and 160ns MLC-access capability

Abstract: Several emerging nonvolatile memories (NVMs) including phase-change RAM (PCRAM) [1–3], MRAM [4–5], and resistive RAM (RRAM) [6–8] have achieved faster operating speeds than embedded Flash. Among those emerging NVMs, RRAM has advantages in faster write time, a larger resistance-ratio (R-ratio), and smaller write power consumption. However, RRAM cells have large cross-die and within-die resistance variations (R-variations) and require low read-mode bitline (BL) bias voltage (V BL-R ) to prevent read disturbance. This work proposes process/resistance variation-insensitive read schemes for embedded RRAM to achieve fast read speeds with high yields. An embedded mega-bit scale (4Mb), single-level-cell (SLC) RRAM macro with sub-8ns read-write random-access time is presented. Multi-level-cell (MLC) operation with 160ns write-ver-ify operation is demonstrated.

Memristive devices for computing

Abstract: Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Metal–Oxide RRAM

Abstract: In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Abstract: Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

A review of Ga2O3 materials, processing, and devices

Abstract: Gallium oxide (Ga2O3) is emerging as a viable candidate for certain classes of power electronics, solar blind UV photodetectors, solar cells, and sensors with capabilities beyond existing technologies due to its large bandgap. It is usually reported that there are five different polymorphs of Ga2O3, namely, the monoclinic (β-Ga2O3), rhombohedral (α), defective spinel (γ), cubic (δ), or orthorhombic (e) structures. Of these, the β-polymorph is the stable form under normal conditions and has been the most widely studied and utilized. Since melt growth techniques can be used to grow bulk crystals of β-GaO3, the cost of producing larger area, uniform substrates is potentially lower compared to the vapor growth techniques used to manufacture bulk crystals of GaN and SiC. The performance of technologically important high voltage rectifiers and enhancement-mode Metal-Oxide Field Effect Transistors benefit from the larger critical electric field of β-Ga2O3 relative to either SiC or GaN. However, the absence of clear demonstrations of p-type doping in Ga2O3, which may be a fundamental issue resulting from the band structure, makes it very difficult to simultaneously achieve low turn-on voltages and ultra-high breakdown. The purpose of this review is to summarize recent advances in the growth, processing, and device performance of the most widely studied polymorph, β-Ga2O3. The role of defects and impurities on the transport and optical properties of bulk, epitaxial, and nanostructures material, the difficulty in p-type doping, and the development of processing techniques like etching, contact formation, dielectrics for gate formation, and passivation are discussed. Areas where continued development is needed to fully exploit the properties of Ga2O3 are identified.