I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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.

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

An object is to provide a semiconductor device of which a manufacturing process is not complicated and by which cost can be suppressed, by forming a thin film transistor using an oxide semiconductor film typified by zinc oxide, and a manufacturing method thereof. For the semiconductor device, a gate electrode is formed over a substrate; a gate insulating film is formed covering the gate electrode; an oxide semiconductor film is formed over the gate insulating film; and a first conductive film and a second conductive film are formed over the oxide semiconductor film. The oxide semiconductor film has at least a crystallized region in a channel region.

Semiconductor device, and manufacturing method thereof

An object of the present invention is to decrease the resistance of a power supply line, to suppress a voltage drop in the power supply line, and to prevent defective display. A connection terminal portion includes a plurality of connection terminals. The plurality of connection terminals is provided with a plurality of connection pads which is part of the connection terminal. The plurality of connection pads includes a first connection pad and a second connection pad having a line width different from that of the first connection pad. Pitches between the plurality of connection pads are equal to each other.

Semiconductor device and display device

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 revie...

https://www.tandfonline.com/doi/pdf/10.1080/23746149.2016.1259585

Neuromorphic computing using non-volatile memory

Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O3 (PZT)/SrRuO3. In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear puls...

Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

We demonstrate, for the first time, the on-chip pattern recognition of a multishaded grayscale image in a neural network circuit with multiple neurons. This pattern recognition is based on a spiking neural network model that uses multiple three-terminal ferroelectric memristors (3T-FeMEMs) as synapses. The synapse chip of the neural network is formed by stacking CMOS circuits and 3T-FeMEMs. The conductance of the 3T-FeMEM is gradually changed in the linear range by varying the amplitude of the applied voltage pulse. Using the analog and nonvolatile conductance change of the 3T-FeMEM as synaptic weight, the matrix patterns are learned after the spike timing-dependent plasticity learning rule. Even when an incomplete multishaded pattern is input to the neural network circuit, it automatically completes and recalls a previously learned pattern.

Ferroelectric Artificial Synapses for Recognition of a Multishaded Image

We have developed a ferroelectric-gate field-effect transistor (FeFET) composed of heteroepitaxially stacked oxide materials. A semiconductor film of ZnO, a ferroelectric film of Pb(Zr,Ti)O3 (PZT), and a bottom gate electrode of SrRuO3 (SRO) are grown on a SrTiO3 (STO) substrate. Structural characterization shows a heteroepitaxy of the fabricated ZnO/PZT/SRO/STO structure with good crystalline quality and the absence of an interface reaction layer. When the polarization direction of the PZT film is downward, all the electrons across the ZnO film are depleted. When the polarization direction is upward, on the other hand, quasi-two-dimensional electron gas is accumulated at the ZnO/PZT interface. The switching of the quasi-two-dimensional electron gas between the two states is confirmed by capacitance–voltage measurements. Drain and source electrodes of Pt/Ti are formed on the ZnO film and in-plane conduction at the ZnO/PZT interface is probed. When gate voltages applied to the bottom electrode are swept between -10 and 10 V, the on/off ratio of drain currents is higher than 105. Such a high on/off ratio is preserved even after 105 s and the extrapolation of the retention behavior predicts a definite memory window of more than 10 years.

Nonvolatile Memory Using Epitaxially Grown Composite-Oxide-Film Technology

A brain-like learning function was implemented in an electronic synapse device using a ferroelectric-gate field effect transistor (FeFET). The FeFET was a bottom-gate type FET with a ZnO channel and a ferroelectric Pb(Zr,Ti)O3 (PZT) gate insulator. The synaptic weight, which is represented by the channel conductance of the FeFET, is updated by applying a gate voltage through a change in the ferroelectric polarization in the PZT. A learning function based on the symmetric spike-timing dependent synaptic plasticity was implemented in the synapse device using the multilevel weight update by applying a pulse gate voltage. The dynamic weighting and learning behavior in the synapse device was observed as a change in the membrane potential in a spiking neuron circuit.

https://iopscience.iop.org/article/10.7567/JJAP.52.04CE06/pdf

Dynamic Observation of Brain-Like Learning in a Ferroelectric Synapse Device

The transition to solid-state Li-ion batteries will enable progress toward energy densities of 1000 W·hour/liter and beyond. Composites of a mesoporous oxide matrix filled with nonvolatile ionic liquid electrolyte fillers have been explored as a solid electrolyte option. However, the simple confinement of electrolyte solutions inside nanometer-sized pores leads to lower ion conductivity as viscosity increases. Here, we demonstrate that the Li-ion conductivity of nanocomposites consisting of a mesoporous silica monolith with an ionic liquid electrolyte filler can be several times higher than that of the pure ionic liquid electrolyte through the introduction of an interfacial ice layer. Strong adsorption and ordering of the ionic liquid molecules render them immobile and solid-like as for the interfacial ice layer itself. The dipole over the adsorbate mesophase layer results in solvation of the Li+ ions for enhanced conduction. The demonstrated principle of ion conduction enhancement can be applied to different ion systems.

https://advances.sciencemag.org/content/advances/6/2/eaav3400.full.pdf

Silica gel solid nanocomposite electrolytes with interfacial conductivity promotion exceeding the bulk Li-ion conductivity of the ionic liquid electrolyte filler

Yukihiro Kaneko

Papers

Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

Ferroelectric Artificial Synapses for Recognition of a Multishaded Image

Nonvolatile Memory Using Epitaxially Grown Composite-Oxide-Film Technology

Dynamic Observation of Brain-Like Learning in a Ferroelectric Synapse Device

Silica gel solid nanocomposite electrolytes with interfacial conductivity promotion exceeding the bulk Li-ion conductivity of the ionic liquid electrolyte filler