Neuromorphic system using thin-film devices

Artificial intelligences are essential concepts in smart societies, and neural networks are typical schemes that imitate human brains. However, conventionally, neural networks are realized using complicated software and high-spec hardware, whose machine size is large and power consumption is also huge. On the other hand, neuromorphic systems are composed only of customized hardware, whose machine size can be small and power consumption can be reduced. Please click Additional Files below to see the full abstract

Mesoporous Silica-Based Materials for Electronics-Oriented Applications

International audienceElectronics, and nanoelectronics in particular, represent one of the most promising branches of technology. The search for novel and more efficient materials seems to be natural here. Thus far, silicon-based devices have been monopolizing this domain. Indeed, it is justified since it allows for significant miniaturization of electronic elements by their densification in integrated circuits. Nevertheless, silicon has some restrictions. Since this material is applied in the bulk form, the miniaturization limit seems to be already reached. Moreover, smaller silicon-based elements (mainly processors) need much more energy and generate significantly more heat than their larger counterparts. In our opinion, the future belongs to nanostructured materials where a proper structure is obtained by means of bottom-up nanotechnology. A great example of a material utilizing nanostructuring is mesoporous silica, which, due to its outstanding properties, can find numerous applications in electronic devices. This focused review is devoted to the application of porous silica-based materials in electronics. We guide the reader through the development and most crucial findings of porous silica from its first synthesis in 1992 to the present. The article describes constant struggle of researchers to find better solutions to supercapacitors, lower the k value or redox-active hybrids while maintaining robust mechanical properties. Finally, the last section refers to ultra-modern applications of silica such as molecular artificial neural networks or super-dense magnetic memory storage

Fabrication and Application of a Polymer Neuromorphic Circuitry Based on Polymer Memristive Devices and Polymer Transistors

Neuromorphic engineering is a discipline that aims to address the shortcomings of today\u27s serial computers, namely large power consumption, susceptibility to physical damage, as well as the need for explicit programming, by applying biologically-inspired principles to develop neural systems with applications such as machine learning and perception, autonomous robotics and generic artificial intelligence. This doctoral dissertation presents work performed fabricating a previously developed type of polymer neuromorphic architecture, termed Polymer Neuromorphic Circuitry (PNC), inspired by the McCulloch-Pitts model of an artificial neuron. The major contribution of this dissertation is a development of processing techniques necessary to realize the Polymer Neuromorphic Circuitry, which required a development of individual polymer electronics elements, as well as customization of fabrication processes necessary for the realization of the circuitry on separate substrates as well as on a single substrate. This is the first demonstration of a fabrication of an entire neuron, and more importantly, a network of such neurons, that includes both the weighting functionality of a synapse and the somatic summing, all realized with polymer electronics technology. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the conventional, inorganic electronics in the form of physical flexibility, low cost and ease of fabrication, manufacturing compatibility with many substrate materials, as well as greater biological compatibility. These advantages were the primary motivation for the choice to fabricate all of the electrical components required to realize the PNC, namely polymer transistors, polymer memristive devices, and polymer resistors, with polymer electronics components. The efficacy of this design is validated by demonstrating that the activation function of a single neuron approximates the sigmoidal function commonly employed by artificial neural networks. The utility of the neuromorphic circuitry is further corroborated by illustrating that a network of such neurons, and even a single neuron, are capable of performing linear classification for a real-life problem

Development and modelling of a versatile active micro-electrode array for high density in-vivo and in-vitro neural signal investigation

The electrophysiological observation of neurological cells has allowed much knowledge to be gathered regarding how living organisms are believed to acquire and process sensation. Although much has been learned about neurons in isolation, there is much more to be discovered in how these neurons communicate within large networks. The challenges of measuring neurological networks at the scale, density and chronic level of non invasiveness required to observe neurological processing and decision making are manifold, however methods have been suggested that have allowed small scale networks to be observed using arrays of micro-fabricated electrodes. These arrays transduce ionic perturbations local to the cell membrane in the extracellular fluid into small electrical signals within the metal that may be measured. A device was designed for optimal electrical matching to the electrode interface and maximal signal preservation of the received extracellular neural signals. Design parameters were developed from electrophysiological computer simulations and experimentally obtained empirical models of the electrode-electrolyte interface. From this information, a novel interface based signal filtering method was developed that enabled high density amplifier interface circuitry to be realised. A novel prototype monolithic active electrode was developed using CMOS microfabrication technology. The device uses the top metallization of a selected process to form the electrode substrate and compact amplification circuitry fabricated directly beneath the electrode to amplify and separate the neural signal from the baseline offsets and noise of the electrode interface. The signal is then buffered for high speed sampling and switched signal routing. Prototype 16 and 256 active electrode array with custom support circuitry is presented at the layout stage for a 20 μm diameter 100 μm pitch electrode array. Each device consumes 26.4 μW of power and contributes 4.509 μV (rms) of noise to the received signal over a controlled bandwidth of 10 Hz - 5 kHz. The research has provided a fundamental insight into the challenges of high density neural network observation, both in the passive and the active manner. The thesis concludes that power consumption is the fundamental limiting factor of high density integrated MEA circuitry; low power dissipation being crucial for the existence of the surface adhered cells under measurement. With transistor sizing, noise and signal slewing each being inversely proportional to the dc supply current and the large power requirements of desirable ancillary circuitry such as analogue-to-digital converters, a situation of compromise is approached that must be carefully considered for specific application design

Design of Neuromemristive Systems for Visual Information Processing

Neuromemristive systems (NMSs) are brain-inspired, adaptive computer architectures based on emerging resistive memory technology (memristors). NMSs adopt a mixed-signal design approach with closely-coupled memory and processing, resulting in high area and energy efficiencies. Previous work suggests that NMSs could even supplant conventional architectures in niche application domains such as visual information processing. However, given the infancy of the field, there are still several obstacles impeding the transition of these systems from theory to practice. This dissertation advances the state of NMS research by addressing open design problems spanning circuit, architecture, and system levels. Novel synapse, neuron, and plasticity circuits are designed to reduce NMSs’ area and power consumption by using current-mode design techniques and exploiting device variability. Circuits are designed in a 45 nm CMOS process with memristor models based on multilevel (W/Ag-chalcogenide/W) and bistable (Ag/GeS2/W) device data. Higher-level behavioral, power, area, and variability models are ported into MATLAB to accelerate the overall simulation time. The circuits designed in this work are integrated into neural network architectures for visual information processing tasks, including feature detection, clustering, and classification. Networks in the NMSs are trained with novel stochastic learning algorithms that achieve 3.5 reduction in circuit area, reduced design complexity, and exhibit similar convergence properties compared to the least-mean-squares algorithm. This work also examines the effects of device-level variations on NMS performance, which has received limited attention in previous work. The impact of device variations is reduced with a partial on-chip training methodology that enables NMSs to be configured with relatively sophisticated algorithms (e.g. resilient backpropagation), while maximizing their area-accuracy tradeoff

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS

In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits. The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis. To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis. Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.Siirretty Doriast

Semiconducting Polymers for Electronic Biosensors and Biological Interfaces

Bioeletronics aims at the direct coupling of biomolecular function units with standard electronic devices. The main limitations of this field are the material needed to interface soft living entities with hard inorganic devices. Conducting polymers enabled the bridging between these two separate worlds, owing to their biocompatibility, soft nature and the ability to be tailored according to the required application. In particular, the intrinsically conductive poly(3,4-ethylenedioxythiophene):poly(styrenesulfonic acid) (PEDOT:PSS) is one of the most promising polymers, having an excellent chemical and thermal stability, reversible doping state and high conductivity. This thesis relies on the use of PEDOT:PSS as semiconducting material for biological interfaces and biosensors. In detail, OECTs were demonstrated to be able to real-time monitor growth and detachment of both strong-barrier and no-barrier cells, according to the patterning of the device active area and the selected geometry. Thus, these devices were employed to assess silver nanoparticles (AgNPs) toxicity effects on cell lines, allowing further insights on citrate-coated AgNPs uptake by the cells and their toxic action, while demonstrating no cytotoxic activity of EG6OH-coated AgNPs. Moreover, PEDOT:PSS OECTs were proved to be capable of detecting oxygen dissolved in KCl or even cell culture medium, in the oxygen partial pressure range of 0-5%. Furthermore, PEDOT:PSS OECTs were biofunctionalized to impart specificity on the device sensing capabilities, through a biochemical functionalization strategy, electrically characterized. The resulting devices showed a proof of concept detection of a fundamental cytokine for cells undergoing osteogenic differentiation. Finally, PEDOT:PSS thickness-controlled films were employed as biocompatible, low-impedance and soft interfaces between the animal nerve and a gold electrode. The introduction of the plasticizer polyethylene glycol (PEG) enhanced the elasticity of the polymer, while keeping good conductivity and low-impedance properties. An in-vivo, chronic recording of the renal sympathetic nerve activity in rats demonstrated the efficiency of the device

Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

Towards Single-Chip Nano-Systems

Important scientific discoveries are being propelled by the advent of nano-scale sensors that capture weak signals from their environment and pass them to complex instrumentation interface circuits for signal detection and processing. The highlight of this research is to investigate fabrication technologies to integrate such precision equipment with nano-sensors on a single complementary metal oxide semiconductor (CMOS) chip. In this context, several demonstration vehicles are proposed. First, an integration technology suitable for a fully integrated flexible microelectrode array has been proposed. A microelectrode array containing a single temperature sensor has been characterized and the versatility under dry/wet, and relaxed/strained conditions has been verified. On-chip instrumentation amplifier has been utilized to improve the temperature sensitivity of the device. While the flexibility of the array has been confirmed by laminating it on a fixed single cell, future experiments are necessary to confirm application of this device for live cell and tissue measurements. The proposed array can potentially attach itself to the pulsating surface of a single living cell or a network of cells to detect their vital signs