Phase Change Memory Model Research Articles

A Robust and Efficient Compact Model for Phase-Change Memory Circuit Simulations

A phase-change memory (PCM) model for robust and efficient simulations of circuits including neuromorphic ones is reported in this work. The features of a hysteretic dynamic resistance in the voltage domain, and the incubation in the crystallization, are covered in the model. The Landau-Khalatnikov (LK)-type equation for ferroelectric is used to develop the PCM hysteresis module. A voltage-controlled relaxation oscillation is successfully simulated for the Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> (GST) PCM. A technique of direct evaluation (DE) is then developed to reformulate the PCM model without any internal node. A significant enhancement of simulation efficiency is achieved compared with the traditional approach without sacrificing the accuracy. The functional correctness of the PCM device model and the acceleration effect in circuit simulations are verified.

Development of Parallel Codes for a Nonlinear Heat Equation in Problems of Phase-Change Memory Simulation

Results are presented from developing a parallel code for a joint solution to the nonlinear three-dimensional heat equation and the Poisson equation in creating a multiscale model for phase-change memory based on amorphous carbon nanofilms (a-C). Their numerical convergence is investigated. The parallel effectiveness of the new code is examined on the IBM BlueGene/P supercomputer.

A SPICE Model of Phase Change Memory for Neuromorphic Circuits

A phase change memory (PCM) model suitable for neuromorphic circuit simulations is developed. A crystallization ratio module is used to track the memory state in the SET process, and an active region radius module is developed to track the continuously varying amorphous region in the RESET process. To converge the simulations with bi-stable memory states, a predictive filament module is proposed using a previous state in iterations of nonlinear circuit matrix under a voltage-driven mode. Both DC and transient analysis are successfully converged in circuits with voltage sources. The spiking-time-dependent-plasticity (STDP) characteristics essential for synaptic PCM are successfully reproduced with SPICE simulations verifying the model's promising applications in neuromorphic circuit designs. Further on, the developed PCM model is applied to propose a neuron circuit topology with lateral inhibitions which is more bionic and capable of distinguishing fuzzy memories. Finally, unsupervised learning of handwritten digits on neuromorphic circuits is simulated to verify the integrity of models in a large-scale-integration circuits. For the first time in literature an emerging memory model is developed and applied successfully in neuromorphic circuit designs, and the model is applicable to flexible designs of neuron circuits for further performance improvements.

A phase-change memory model for neuromorphic computing

Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels in a controllable way makes PCM devices particularly well-suited for synaptic realizations in neuromorphic computing. A key attribute that enables this application is the progressive crystallization of the phase-change material and subsequent increase in device conductance by the successive application of appropriate electrical pulses. There is significant inter- and intra-device randomness associated with this cumulative conductance evolution, and it is essential to develop a statistical model to capture this. PCM also exhibits a temporal evolution of the conductance values (drift), which could also influence applications in neuromorphic computing. In this paper, we have developed a statistical model that describes both the cumulative conductance evolution and conductance drift. This model is based on extensive characterization work on 10 000 memory devices. Finally, the model is used to simulate the supervised training of both spiking and non-spiking artificial neuronal networks.

Comprehensive Phase-Change Memory Compact Model for Circuit Simulation

In this paper, a new continuous multilevel compact model for phase-change memory (PCM) is proposed. It is based on the modified rate equations with the introduction of a variable related to material melting. The model is evaluated using a large set of dynamic measurements and shows a good accuracy with a single model card. All fitting parameters are discussed, and their impacts are detailed. Full circuit simulation is performed. Good convergence and fast simulation time suggest that this new compact model can be exploited for PCM circuit design.

Flux–Charge Memristor Model for Phase Change Memory

Phase-change memory (PCM) is one of the most promising non-volatile memory technologies and is finding applications in areas such as storage-class memory and emerging non-von Neumann computing systems. Even though powerful physics-based models have been developed for these devices, there is a lack of simple and accurate circuit models to describe these elements. In this brief, we exploit memristor theory to obtain a simple and reliable circuit model based on electrical variables such as charge and flux . This model is based on experimental measurements of PCM devices fabricated in the 90 nm technology node.

Terabit-per-square-inch scanning probe phase-change memory model based on nucleation-growth theory

Scanning electrical probe memory using Ge2Sb2Te5 has been considered as one of the promising contenders for next-generation data storage device, due to its ultra-high capacity, short access time, and low power consumption. Therefore, a numerical model based on classical nucleation-growth theory was developed to simulate the crystallisation behaviour of Ge2Sb2Te5 material in order to help understanding the physics mechanism of scanning electrical probe memory and thus provide the performance improvement. The crystallisation of Ge2Sb2Te5 in this model is first induced by the advent of the nuclei determined by nucleation probability, followed by the subsequent growth relying on the growth velocity. Results obtained from this model are in a good agreement with the experimental counterpart, revealing a potential of storage density of terabit per square inch.

An SPICE model for phase-change memory simulations

Along with a series of research works on the physical prototype and properties of the memory cell, an SPICE model for phase-change memory (PCM) simulations based on Verilog-A language is presented. By handling it with the heat distribution algorithm, threshold switching theory and the crystallization kinetic model, the proposed SPICE model can effectively reproduce the physical behaviors of the phase-change memory cell. In particular, it can emulate the cell's temperature curve and crystallinity profile during the programming process, which can enable us to clearly understand the PCM's working principle and program process.