Design considerations for a monolithic, GaAs, dual-mode, QPSK/QASK, high-throughput rate transceiver

A monolithic, GaAs, dual mode, quadrature amplitude shift keying and quadrature phase shift keying transceiver with one and two billion bits per second data rate is being considered to achieve a low power, small and ultra high speed communication system for satellite as well as terrestrial purposes. Recent GaAs integrated circuit achievements are surveyed and their constituent device types are evaluated. Design considerations, on an elemental level, of the entire modem are further included for monolithic realization with practical fabrication techniques. Numerous device types, with practical monolithic compatability, are used in the design of functional blocks with sufficient performances for realization of the transceiver

Tactile sensing chips with POSFET array and integrated interface electronics

This work presents the advanced version of novel POSFET (Piezoelectric Oxide Semiconductor Field Effect Transistor) devices based tactile sensing chip. The new version of the tactile sensing chip presented here comprises of a 4 x 4 array of POSFET touch sensing devices and integrated interface electronics (i.e. multiplexers, high compliance current sinks and voltage output buffers). The chip also includes four temperature diodes for the measurement of contact temperature. Various components on the chip have been characterized systematically and the overall operation of the tactile sensing system has been evaluated. With new design the POSFET devices have improved performance (i.e. linear response in the dynamic contact forces range of 0.01–3N and sensitivity (without amplification) of 102.4 mV/N), which is more than twice the performance of their previous implementations. The integrated interface electronics result in reduced interconnections which otherwise would be needed to connect the POSFET array with off-chip interface electronic circuitry. This research paves the way for CMOS (Complementary Metal Oxide Semiconductor) implementation of full on-chip tactile sensing systems based on POSFETs

Flexible Electronics Based on Solution Processable Organic Semiconductors and Colloidal Semiconductor Nanocrystals

Solution-processable semiconductors hold great potential for the large-area, low-cost fabrication of flexible electronics. Recent advances in flexible electronics have introduced new functional devices such as light-weight displays and conformal sensors. However, key challenges remain to develop flexible devices from emerging materials that use simple fabrication processes and have high-performance. In this thesis, we first use a solution-processable organic semiconductor to build field-effect transistors on large-area plastic with mobility of 0.1 cm^2/Vs. Combined with passive components, we are able to build voltage amplifiers to capture few mV amplitude bio-signals. This work provides a proof of concept on applying solution processable materials in flexible circuits. In the second part of the thesis, we introduce colloidal CdSe nanocrystals (NCs) as solution-processable inks of semiconductor thin film devices. By strongly coupling and doping the CdSe NC thin films, we demonstrate high-performance, flexible nanocrystal field-effect transistors (NC-FETs) with mobility greater than 20 cm^2/Vs under 2V supply. Using these NC-FETs as building blocks, we demonstrate the first flexible nanocrystal integrated circuits (NCICs) with switching speed of 600 µsec. To design reliable integrated circuits with low-noise, we characterize the flicker noise amplitude and origin. We find the figure of merit for noise, the Hooge parameter, to be 3 x 10^-2 for CdSe NC-FETs, comparable to other emerging solution processable organic semiconductors and promising for low-noise circuit applications.As most of NCs are reactive and their devices tend to degrade in air, we develop processes that allow manipulation of the NCs in ambient atmosphere without compromising device performance. These processes open up opportunities for NC-based devices to be fabricated over large area using photolithography. By scaling the devices and reducing device parasitics, we are able to fabricate hundreds of NC-FETs on wafer-scale substrates and integrate them as circuits. We demonstrate voltage amplifiers with bandwidths of a few kHz and ring-oscillators with a stage delay of 3 µsec. We also show functional NCICs NOR and NAND logic. This thesis demonstrates the use of colloidal NCs to realize flexible, large-area circuits and the feasibility of more advanced analog and digital NCICs built on flexible substrates for various applications

3D printed neuromorphic sensing systems

Thanks to the high energy efficiency, neuromorphic devices are spotlighted recently by mimicking the calculation principle of the human brain through the parallel computation and the memory function. Various bio-inspired \u27in-memory computing\u27 (IMC) devices were developed during the past decades, such as synaptic transistors for artificial synapses. By integrating with specific sensors, neuromorphic sensing systems are achievable with the bio-inspired signal perception function. A signal perception process is possible by a combination of stimuli sensing, signal conversion/transmission, and signal processing. However, most neuromorphic sensing systems were demonstrated without signal conversion/transmission functions. Therefore, those cannot fully mimic the function provides by the sensory neuron in the biological system. This thesis aims to design a neuromorphic sensing system with a complete function as biological sensory neurons. To reach such a target, 3D printed sensors, electrical oscillators, and synaptic transistors were developed as functions of artificial receptors, artificial neurons, and artificial synapses, respectively. Moreover, since the 3D printing technology has demonstrated a facile process due to fast prototyping, the proposed 3D neuromorphic sensing system was designed as a 3D integrated structure and fabricated by 3D printing technologies. A novel multi-axis robot 3D printing system was also utilized to increase the fabrication efficiency with the capability of printing on vertical and tilted surfaces seamlessly. Furthermore, the developed 3D neuromorphic system was easily adapted to the application of tactile sensing. A portable neuromorphic system was integrated with a tactile sensing system for the intelligent tactile sensing application of the humanoid robot. Finally, the bio-inspired reflex arc for the unconscious response was also demonstrated by training the neuromorphic tactile sensing system

Perspective: Organic electronic materials and devices for neuromorphic engineering

Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware platforms are expected to emerge to answer to the new needs and challenges of our societies. In this revolution, lots of candidates technologies are explored and will require leveraging of the pro and cons. In this perspective paper belonging to the special issue on neuromorphic engineering of Journal of Applied Physics, we focus on the current achievements in the field of organic electronics and the potentialities and specificities of this research field. We highlight how unique material features available through organic materials can be used to engineer useful and promising bioinspired devices and circuits. We also discuss about the opportunities that organic electronic are offering for future research directions in the neuromorphic engineering field

Analog Printed Spiking Neuromorphic Circuit

Biologically-inspired Spiking Neural Networks have emerged as a promising avenue for energy-efficient, high-performance neuromorphic computing. With the demand for highly-customized and cost-effective solutions in emerging application domains like soft robotics, wearables, or IoT-devices, Printed Electronics has emerged as an alternative to traditional silicon technologies leveraging soft materials and flexible substrates. In this paper, we propose an energy-efficient analog printed spiking neuromorphic circuit and a corresponding learning algorithm. Simulations on 13 benchmark datasets show an average of 3.86× power improvement with similar classification accuracy compared to previous works