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.

Ultrafast characterization of phase-change material crystallization properties in the melt-quenched amorphous phase.

Phase change materials are widely considered for application in nonvolatile memories because of their ability to achieve phase transformation in the nanosecond time scale. However, the knowledge of fast crystallization dynamics in these materials is limited because of the lack of fast and accurate temperature control methods. In this work, we have developed an experimental methodology that enables ultrafast characterization of phase-change dynamics on a more technologically relevant melt-quenched amorphous phase using practical device structures. We have extracted the crystallization growth velocity (U) in a functional capped phase change memory (PCM) device over 8 orders of magnitude (10(-10) < U < 10(-1) m/s) spanning a wide temperature range (415 < T < 580 K). We also observed direct evidence of non-Arrhenius crystallization behavior in programmed PCM devices at very high heating rates (>10(8) K/s), which reveals the extreme fragility of Ge2Sb2Te5 in its supercooled liquid phase. Furthermore, these crystallization properties were studied as a function of device programming cycles, and the results show degradation in the cell retention properties due to elemental segregation. The above experiments are enabled by the use of an on-chip fast heater and thermometer called as microthermal stage (MTS) integrated with a vertical phase change memory (PCM) cell. The temperature at the PCM layer can be controlled up to 600 K using MTS and with a thermal time constant of 800 ns, leading to heating rates ∼10(8) K/s that are close to the typical device operating conditions during PCM programming. The MTS allows us to independently control the electrical and thermal aspects of phase transformation (inseparable in a conventional PCM cell) and extract the temperature dependence of key material properties in real PCM devices.

Nanoscale phase change memory materials.

Phase change memory materials store information through their reversible transitions between crystalline and amorphous states. For typical metal chalcogenide compounds, their phase transition properties directly impact critical memory characteristics and the manipulation of these is a major focus in the field. Here, we discuss recent work that explores the tuning of such properties by scaling the materials to nanoscale dimensions, including fabrication and synthetic strategies used to produce nanoscale phase change memory materials. The trends that emerge are relevant to understanding how such memory technologies will function as they scale to ever smaller dimensions and also suggest new approaches to designing materials for phase change applications. Finally, the challenges and opportunities raised by integrating nanoscale phase change materials into switching devices are discussed.

Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.

Brain-inspired computing is an emerging field, which aims to extend the capabilities of information technology beyond digital logic. A compact nanoscale device, emulating biological synapses, is needed as the building block for brain-like computational systems. Here, we report a new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications. We utilize continuous resistance transitions in phase change materials to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule. We demonstrate different forms of spike-timing-dependent plasticity using the same nanoscale synapse with picojoule level energy consumption.