SangBum Kim

Phase Change Memory

In this paper, recent progress of phase change memory (PCM) is reviewed. The electrical and thermal properties of phase change materials are surveyed with a focus on the scalability of the materials and their impact on device design. Innovations in the device structure, memory cell selector, and strategies for achieving multibit operation and 3-D, multilayer high-density memory arrays are described. The scaling properties of PCM are illustrated with recent experimental results using special device test structures and novel material synthesis. Factors affecting the reliability of PCM are discussed.

Thickness and stoichiometry dependence of the thermal conductivity of GeSbTe films

Thermal conduction in GeSbTe films strongly influences the writing energy and time for phase change memory (PCM) technology. This study measures the thermal conductivity of Ge2Sb2Te5 between 25 and 340°C for layers with thicknesses near 60, 120, and 350nm. A strong thickness dependence of the thermal conductivity is attributed to a combination of thermal boundary resistance (TBR) and microstructural imperfections. Stoichiometric variations significantly alter the phase transition temperatures but do not strongly impact the thermal conductivity at a given temperature. This work makes progress on extracting the TBR for Ge2Sb2Te5 films, which is a critical unknown parameter for PCM simulations.

Thermal Boundary Resistance Measurements for Phase-Change Memory Devices

Thermal interfaces play a key role in determining the programming energy of phase-change memory (PCM) devices. This letter reports the picosecond thermoreflectance measurements of thermal boundary resistance (TBR) at TiN/GST and Al/TiN interfaces, as well as the intrinsic thermal conductivity measurements of fcc GST between 30°C and 325°C. The TiN/GST TBR decreases with temperature from ~26 to ~18 m2·K/GW, and the Al/TiN ranges from ~7 to 2.4 m2·K/GW. A TBR of 10 m2·K/GW is equivalent in thermal resistance to ~192 nm of TiN. The fcc GST conductivity increases with temperature between ~0.44 and 0.59 W/m/K. A detailed understanding of TBR is essential for optimizing the PCM technology.

An Integrated Phase Change Memory Cell With Ge Nanowire Diode For Cross-Point Memory

We demonstrate a novel phase change memory cell utilizing doped Ge nanowire pn-junction diode both as a bottom electrode and a memory cell selection device. This memory cell can be used for a cross-point memory array with diode selection. Using selective growth of isolated vertical nanowires in each cell, we have minimized the contact area below the lithography limit. A very low SET programming current of 10s of muA was achieved. RESET/SET resistance ratio of 100x was obtained. The diode provides 100x isolation between forward and reverse bias in the SET state.

Neuromorphic Computing Using Non-Volatile Memory

Abstract Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability. Graphical Abstract

Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.

Recent Progress in Phase-Change Memory Technology

We survey progress in the PCM field over the past five years, ranging from large-scale PCM demonstrations to materials improvements for high-temperature retention and faster switching. Both materials and new cell designs that support lower-power switching are discussed, as well as higher reliability for long cycling endurance. Two paths towards higher density are discussed: through 3D integration by the combination of PCM and 3D-capable access devices, and through multiple bits per cell, by understanding and managing resistance drift caused by structural relaxation of the amorphous phase. We also briefly survey work in the nascent field of brain-inspired neuromorphic systems that use PCM to implement non-Von Neumann computing.

Analysis of Temperature in Phase Change Memory Scaling

We analyze constant-voltage isotropic and non-isotropic scaling issues for phase change memory (PCM) based on electrothermal physics. Various analytical and simulation models of general and typical PCM cells that support the analysis is also provided. The analysis shows that the maximum temperature in the PCM cell, which is a key parameter for PCM operation, is independent of geometrical sizes and depends only on the voltage and material properties. This leads to the minimum programming voltage concept, which is determined by material properties of the phase change material. Constant-voltage scaling, electrothermal modeling, ovonic unified memory (OUM), phase change memory (PCM, phase change random access memory, PRAM), proximity disturbance, thermal disturbance.

Resistance and Threshold Switching Voltage Drift Behavior in Phase-Change Memory and Their Temperature Dependence at Microsecond Time Scales Studied Using a Micro-Thermal Stage

We study the drift behavior of RESET resistance RRESET and threshold switching voltage Vth in phase-change memory (PCM) and their temperature dependence. To extend the temperature-dependent measurement to microsecond time scales, we integrate an innovative micro-thermal stage (MTS) on the PCM cell. The MTS changes the temperature of the programmed region of the PCM cell within a few microseconds by placing the Pt heater in close proximity of the programmed region. First, we experimentally verify the existing phenomenological RRESET and Vth drift model for constant annealing temperature at various temperatures between 25°C and 185°C down to 100 μs and show that the measured temperature dependence of the drift coefficient agrees well with what is expected from the existing drift models. Based on the existing drift model for a constant annealing temperature, we derive the analytical expression for the RRESET drift for time-varying annealing temperature and experimentally verify the analytical expression. The derived analytical expression is important to understand the impact of thermal disturbance on PCM reliability such as variations in RRESET and Vth.

Phonon and electron transport through Ge2Sb2Te5 films and interfaces bounded by metals

While atomic vibrations dominate thermal conduction in the amorphous and face-centered cubic phases of Ge2Sb2Te5, electrons dominate in the hexagonal closed-packed (hcp) phase. Here we separate the electron and phonon contributions to the interface and volume thermal resistances for the three phases using time-domain thermoreflectance and electrical contact resistance measurements. Even when electrons dominate film-normal volume conduction (i.e., 70% for the hcp phase), their contribution to interface heat conduction is overwhelmed by phonons for high-quality interfaces with metallic TiN.