Chung Hon Lam

Phase-change random access memory: a scalable technology

Nonvolatile RAM using resistance contrast in phase-change materials [or phase-change RAM (PCRAM)] is a promising technology for future storage-class memory. However, such a technology can succeed only if it can scale smaller in size, given the increasingly tiny memory cells that are projected for future technology nodes (i.e., generations). We first discuss the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms. We then discuss experiments that directly address the scaling properties of the phase-change materials themselves, including studies of phase transitions in both nanoparticles and ultrathin films as a function of particle size and film thickness. This work in materials directly motivated the successful creation of a series of prototype PCRAM devices, which have been fabricated and tested at phase-change material cross-sections with extremely small dimensions as low as 3 nm × 20 nm. These device measurements provide a clear demonstration of the excellent scaling potential offered by this technology, and they are also consistent with the scaling behavior predicted by extensive device simulations. Finally, we discuss issues of device integration and cell design, manufacturability, and reliability.

Ultra-Thin Phase-Change Bridge Memory Device Using GeSb

An ultra-thin phase-change bridge (PCB) memory cell, implemented with doped GeSb, is shown with < 100muA RESET current. The device concept provides for simplified scaling to small cross-sectional area (60nm2) through ultra-thin (3nm) films; the doped GeSb phase-change material offers the potential for both fast crystallization and good data retention

Multilevel phase-change memory

Phase-change memory (PCM) has emerged in recent years as one among the most attractive technologies for future non-volatile solid-state memory. PCM relies on the reversible phase transition in chalcogenide materials between different states, i.e., amorphous and poly-crystalline, which are characterized by very different electrical properties. Multilevel storage, namely storage of multiple bits in a memory cell, is a key factor for the competitiveness of PCM technology in the nonvolatile memory market. This paper presents experimental characterization of multilevel PCM devices and addresses the feasibility and reliability issues of multilevel storage using adaptive program-and-verify schemes.

Novel One-Mask Self-Heating Pillar Phase Change Memory

A novel Pillar phase change memory based on fully integrated test arrays in 180nm CMOS technology has been successfully fabricated. A current-confining Pillar structure leads to a self-heating at the center of the chalcogenide layer, and needs only one additional mask level for its fabrication. Switching characteristics with write currents less than 900muA at 75nm diameter and multilevel operation are reported

Nanoscale electronic synapses using phase change devices

The memory capacity, computational power, communication bandwidth, energy consumption, and physical size of the brain all tend to scale with the number of synapses, which outnumber neurons by a factor of 10,000. Although progress in cortical simulations using modern digital computers has been rapid, the essential disparity between the classical von Neumann computer architecture and the computational fabric of the nervous system makes large-scale simulations expensive, power hungry, and time consuming. Over the last three decades, CMOS-based neuromorphic implementations of “electronic cortex” have emerged as an energy efficient alternative for modeling neuronal behavior. However, the key ingredient for electronic implementation of any self-learning system—programmable, plastic Hebbian synapses scalable to biological densities—has remained elusive. We demonstrate the viability of implementing such electronic synapses using nanoscale phase change devices. We introduce novel programming schemes for modulation of device conductance to closely mimic the phenomenon of Spike Timing Dependent Plasticity (STDP) observed biologically, and verify through simulations that such plastic phase change devices should support simple correlative learning in networks of spiking neurons. Our devices, when arranged in a crossbar array architecture, could enable the development of synaptronic systems that approach the density (∼1011 synapses per sq cm) and energy efficiency (consuming ∼1pJ per synaptic programming event) of the human brain.

Transition-metal-oxide-based resistance-change memories

We provide a status report on the development of perovskite-based transition-metal-oxide resistance-change memories. We focus on bipolar resistance switching observed in Cr-doped SrTiO3 memory cells with dimensions ranging from bulk single crystals to CMOS integrated nanoscale devices. We also discuss electronic and ionic processes during electroforming and resistance switching, as evidenced from electron-parametric resonance (EPR), x-ray absorption spectroscopy, electroluminescence spectroscopy, thermal imaging, and transport experiments. EPR in combination with electroluminescence reveals electron trapping and detrapping processes at the Cr site. Results of x-ray absorption experiments prove that the microscopic origin of the electroforming, that is, the insulator-to-metal transition, is the creation of oxygen vacancies. Cr-doped SrTiO3 memory cells exhibit short programming times (≤100 ns) and low programming currents (<100 µA) with up to 105 write and erase cycles.

Programming algorithms for multilevel phase-change memory

Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.

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.

Ultralow-power SRAM technology

An ultralow-standby-power technology has been developed in both 0.18-µm and 0.13-µm lithography nodes for embedded and standalone SRAM applications. The ultralow-leakage six-transistor (6T) SRAM cell sizes are 4.81 µm2 and 2.34 µm2, corresponding respectively to the 0.18-µm and 0.13-µm design dimensions. The measured array standby leakage is equal to an average cell leakage current of less than 50 fA per cell at 1.5 V, 25°C and is less than 400 fA per cell at 1.5 V, 85°C. Dual gate oxides of 2.9 nm and 5.2 nm provide optimized cell leakage, I/O compatibility, and performance. Analyses of the critical parasitic leakage components and paths within the 6T SRAM cell are reviewed in this paper. In addition to the well-known gate-oxide leakage limitation for ULP technologies, three additional limits facing future scaled ULP technologies are discussed.

NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning

We demonstrate a neuromorphic core with 64k-cell phase change memory (PCM) synaptic array (256 axons by 256 dendrites) with in-situ learning capability. 256 configurable on-chip neuron circuits perform leaky integrate and fire (LIF) and synaptic weight update based on spike-timing dependent plasticity (STDP). 2T-1R PCM unit cell design separates LIF and STDP learning paths, minimizing neuron circuit size. The circuit implementation of STDP learning algorithm along with 2T-1R structure enables both LIF and STDP learning to operate asynchronously and simultaneously within the array, avoiding additional complication and power consumption associated with timing schemes. We show hardware demonstration of in-situ learning with large representational capacity, enabled by large array size and analog synaptic weights of PCM cells.