Tiffany Moy

Enabling Scalable Hybrid Systems: Architectures for Exploiting Large-Area Electronics in Applications

By enabling diverse and large-scale transducers, large-area electronics raises the potential for electronic systems to interact much more extensively with the physical world than is possible today. This can substantially expand the scope of applications, both in number and in value. But first, translation into applications requires a base of system functions (instrumentation, computation, power management, communication). These cannot be realized on the desired scale by large-area electronics alone. It is necessary to combine large-area electronics with high-performance, high-efficiency technologies, such as crystalline silicon CMOS, within hybrid systems. Scalable hybrid systems require rethinking the subsystem architectures from the start by considering how the technologies should be interfaced, on both a functional and physical level. To explore platform architectures along with the supporting circuits and devices, we consider as an application driver, a self-powered sheet for high-resolution structural health monitoring (of bridges and buildings). Top-down evaluation of design alternatives within the hybrid design space and pursuit of template architectures exposes circuit functions and device optimizations traditionally overlooked by bottom-up approaches alone.

Thin-film circuits for scalable interfacing between large-area electronics and CMOS ICs

Hybrid systems based on large-area electronics (LAE) and CMOS ICs aim to exploit the complementary strengths of the two technologies: the scalability of LAE for forming interconnects and transducers (for sensing and energy harvesting), and the energy efficiency of CMOS for instrumentation and computation. The viability of large-scale systems depends on maximizing the robustness and minimizing the number of interfaces between the LAE and CMOS domains. To maximize robustness, inductive and capacitive coupling has been explored, avoiding the need for metallurgical bonding [1]. To minimize the number of interfaces, a method to access and readout individual sensors via minimal coupling channels, is crucial. In this abstract, we present a thin-film transistor (TFT) based scanning circuit that requires only three capacitively-coupled control signals from the IC to sequentially access an arbitrarily large number of LAE sensors, enabling a single readout interface (Fig. 1). A key attribute of the presented circuit is the low power consumption, which remains nearly constant even as the number of sensors scales.

16.2 A large-area image sensing and detection system based on embedded thin-film classifiers

This paper presents a large-area image sensing and detection system that integrates, on glass, sensors and thin-film transistor (TFT) circuits for classifying images from sensor data. Large-area electronics (LAE) enables the formation of millions of sensors spanning physically large areas; however, to perform processing functions, thousands of sensor signals must be interfaced to CMOS ICs, posing a critical limitation to system scalability. This work presents an approach whereby image detection of shapes is performed using simple circuits in the LAE domain based on amorphous silicon (a-Si) TFTs. This reduces the interfaces to the CMOS domain. The limited computational capability of TFT circuits as well as high variability and high density of process defects affecting TFTs and sensors is overcome using a machine-learning algorithm known as error-adaptive classifier boosting (EACB) to form embedded weak classifiers. Through EACB, we show that high-dimensional sensor data from a-Si photoconductors can be reduced to a small number of weak-classifier decisions, which can then be combined in CMOS to achieve strong-classifier performance. For demonstration, a system classifying five shapes achieves performance of >85%/>95% [true-positive (tp)/true-negative (tn) rates] [near the level of an ideal software-implemented support vector machine (SVM) classifier], while the total number of signals from 36 sensors in the LAE domain is reduced by

An EEG Acquisition and Biomarker-Extraction System Using Low-Noise-Amplifier and Compressive-Sensing Circuits Based on Flexible, Thin-Film Electronics

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A Large-Area Image Sensing and Detection System Based on Embedded Thin-Film Classifiers

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Large-Area Microphone Array for Audio Source Separation Based on a Hybrid Architecture Exploiting Thin-Film Electronics and CMOS

This paper presents an electroencephalogram (EEG) acquisition and biomarker-extraction system based on flexible, thin-film electronics. There exist commercial, single-use, flexible, pre-gelled electrode arrays; however, these are fully passive, requiring cabling to transfer sensitive, low-amplitude signals to external electronics for readout and processing. This work presents an active EEG acquisition system on flex, based on amorphous silicon (a-Si) thin-film transistors (TFTs). The system incorporates embedded chopper-stabilized a-Si TFT low-noise amplifiers, to enhance signal integrity, and a-Si TFT compressive-sensing scanning circuits, to enable reduction of EEG data from many channels onto a single interface, for subsequent processing by a CMOS IC. Further, the system uses an algorithm, by which spectral-energy features, a key EEG biomarker, are extracted directly from the compressed signals. We demonstrate a prototype, performing EEG acquisition from a human subject, and compressed EEG data. The TFT amplifier achieves a noise PSD of 230 nV/√Hz. reconstruction and seizure detection via analog replay of patient Seizure detection, at up to 64× compression, achieves error rates <;8%. Reconstruction is demonstrated at up to 8× compression.

Integrating and Interfacing Flexible Electronics in Hybrid Large-Area Systems

Large-area electronics (LAE) enables the formation of a large number of sensors capable of spanning dimensions on the order of square meters. An example is X-ray imagers, which have been scaling both in dimension and number of sensors, today reaching millions of pixels. However, processing of the sensor data requires interfacing thousands of signals to CMOS ICs, because implementation of complex functions in LAE has proven unviable due to the low electrical performance and inherent variability of the active devices available, namely amorphous silicon (a-Si) thin-film transistors (TFTs) on glass. Envisioning applications that perform sensing on even greater scales, this work presents an approach whereby high-quality image detection is performed directly in the LAE domain using TFTs. The high variability and number of process defects affecting both the TFTs and sensors are overcome using a machine-learning algorithm known as Adaptive Boosting (AdaBoost) [1] to form an embedded classifier. Through AdaBoost, we show that high-dimensional sensor data can be reduced to a small number of weak-classifier decisions, which can then be combined in the CMOS domain to generate a strong-classifier decision.

A Thin-Film, Large-Area Sensing and Compression System for Image Detection

We present a system for reconstructing-independent voice commands from two simultaneous speakers, based on an array of spatially distributed microphones. It adopts a hybrid architecture, combining large-area electronics (LAE), which enables a physically expansive array (> 1m width), and a CMOS IC, which provides superior transistors for readout and signal processing. The array enables us to: 1) select microphones closest to the speakers to receive the highest SNR signal; 2) use multiple spatially diverse microphones to enhance robustness to variations due to microphones and sound propagation in a practical room. Each channel consists of a thin-film transducer formed from polyvinylidene fluoride (PVDF), a piezopolymer, and a localized amplifier composed of amorphous silicon (a-Si) thin-film transistors (TFTs). Each channel is sequentially sampled by a TFT scanning circuit, to reduce the number of interfaces between the large-area electronics (LAE) and CMOS IC. A reconstruction algorithm is proposed, which exploits the measured transfer function between each speaker and microphone, to separate two simultaneous speakers. The algorithm overcomes 1) sampling-rate limitations of the scanning circuits and 2) sensitivities to microphone placement and directionality. An entire system with eight channels is demonstrated, acquiring and reconstructing two simultaneous audio signals at 2 m distance from the array achieving a signal-to-interferer (SIR) ratio improvement of ~12 dB.

Reconstruction of multiple-user voice commands using a hybrid system based on thin-film electronics and CMOS

An approach to creating large-area systems is described that combines flexible thin-film electronic sensor surfaces with complementary metal-oxide-semi-conductor (CMOS) integrated circuits (ICs). Complete systems are built by lamination of multiple layers, consisting of thin-film subsystems and CMOS ICs on a passive flexible substrate. A flexible passive backplane provides in-plane interconnections. Via-type interconnections between stacked layers are made by inductive or capacitive coupling. Steps and testing techniques, from devices and circuits to fully integrated hybrid systems, are illustrated.

16.4 A flexible EEG acquisition and biomarker extraction system based on thin-film electronics

This paper presents a sensing and compression system for image detection, based on large-area electronics (LAE). LAE allows us to create expansive, yet highly-dense arrays of sensors, enabling integration of millions of pixels. However, the thin-film transistors (TFTs) available in LAE have low performance and high variability, requiring the sensor data to be fed to CMOS ICs for processing. This results in a large number of interconnections, which raises system cost, and limits system scalability and robustness. To overcome this, the presented system employs random projection, a method from statistical signal processing, to compress the pixel data from a large array of image sensors in the LAE domain using TFTs. Random projection preserves the information required for subsequent classification, and, as we show, is highly tolerant to device-level variabilities and amenable to parallelized implementation. The system integrates an amorphous-silicon (a-Si) TFT compression circuit with an array of a-Si photoconductors, representing an 80 × 80 active matrix, performing up to 80× compression of the 80 signal interfaces. For demonstration, image classification of handwritten digits from the MNIST database is performed, achieving average error rates of 2-25% for 8-80× compression (e.g., 7% at 20× compression).