Adaptive Integrated Microsystems Laboratory

"Pushing the Limits of Hybrid Signal Processing"

Our research explores new frontiers in non-conventional signal processing techniques on CMOS and hybrid substrates. These include approaching limits of energy efficiency, sensing and resolution by exploiting computational primitives inherent in the physics of devices, sensors and the underlying noise processes and adaptation.

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Details of each research topic can be found in the representative papers listed with each project.

Multiple-input Multiple-output (MIMO) analog-to-digital converters

With advances in fabrication technology, an ever increasing number of sensors can now be integrated within a single package. Examples of such sensors include microelectrode arrays and microphone arrays. In a classical data acquisition paradigm each sensor channel is treated independently with regard to analog-to-digital conversion and any intelligent processing is delegated to a backend digital signal processor. However, in micro/nano-scale high-density sensor arrays, the detrimental effect of finite resolution on signal processing cannot be ignored, especially if the objective is to mine hidden/weak signatures in high-dimensional analog data. Our aim is to explore techniques for integrating machine learning algorithms directly with analog-to-digital conversion such that resolution can be dynamically allocated across sensor channels to achieve superior system performance compared to conventional architectures.

• Sigma-delta learning based MIMO-ADC algorithm is described in this TCAS-I paper.
• Super-resolution MIMO-ADC using Sigma-delta learning is described in this TSP paper.
• Application of Sigma-Delta MIMO-ADC for far-field acoustic source localization can be found in this TCAS-I paper.

Keyword: Multiple-input Multiple-output Analog-to-digital Converters, Acoustic Sensor Arrays, Sigma-delta learning, High-dimensional quantizers, Analog-to-information converters.

Sponsors: National Institute of Health, Johns Hopkins Applied Physics Laboratory, MSU-IRGP

Bias-Scalable Analog Signal Processors

In analog and digital VLSI, design methods typically follow a top-down approach where proven algorithms are mapped onto optimized computing hardware. In this research, we are investigating a bottom-up approach where computing paradigms are inherent in hardware can be used for designing specialized algorithms. This is particularly relevant for analog VLSI (aVLSI) where the computational hardware is always approximate and is heavily dependent on the device physics. Our objective is to use universal conservation principles (charge, mass or current) to approximate well known functions and then suitably modify the algorithms to achieve the desired or even superior performance. We are using a novel approximation technique called "margin propagation" to design energy scalable LDPC decoders that can be used for a wide variety of applications ranging from sensor networks to wireless base-stations. In this regard, this study is unique because it investigates the three way trade-off between energy efficiency, signal-to-noise ratio and bit-error-rate performance, where as traditional approaches only focus on SNR and BER trade-offs. We are also translating the findings of the theoretical study into practical implementations on silicon.

• Margin-propagation based synthesis of bias-scalable circuits can be found in the TCAS-I paper.
• Margin-propagation based LDPC decoder implementation can be found in the JSSC paper.

Keyword: Margin propagation, bias-scalable circuits, translinear circuits, moderate-inversion circuits, weak-inversion circuits, low-density parity check, analog decoders.

Sponsors: National Science Foundation.

Floating-gate and Nanowatt Analog Circuits

Floating-gates provide a compact method for analog storage and for generating on-chip biases. In this research we are exploring new topologies of floating-gate circuits which are robust to real-world operating conditions. Also, we are exploring novel sub-threshold analog circuits which operate using transistor leakage currents (pico and femtoamperes currents) and implement sub-microwatt computing systems.

• Implementation of a Sub-microwatt Analog Pattern Classifier can be found in this JSSC paper.
• Novel Translinear Ohm's Law Principle is described in this Electronics Letter paper.

Keyword: Floating-gate circuits, translinear circuits, current references, bias generators, sub-threshold CMOS circuits.

Sponsors: National Science Foundation.

Energy Scavenging Circuits and Sensors

In this project we are investigating circuit and sensor topologies that can harvest the power directly from the signal being sensed. As a result such circuits and systems can be used for long-term surveillance and monitoring where batteries are impossible to deploy and remote power delivery methods are impractical. In particular, we are developing floating-gate circuits that can be used for long-term health monitoring of biomechanical implants (for example, hip or knee implants) with operational life greater than 10 years.

• Piezo-driven ionized hot-electron injection (p-IHEI) for self-powered sensing can be found in the TCAS-I paper.
• p-IHEI principle for fatigue monitoring in biomechanical implants can be found in the TBioCAS paper.

Keyword: Self-powering, floating-gate transistors, piezoelectricity, energy scavenging, nanowatt, structural health monitoring, implantable sensors.

Sponsors: National Science Foundation, Johns Hopkins Applied Physics Laboratory, Federal Highway Administration

Forward Error-Correcting Biosensors

Environmental variability and stochastic interaction between bio-molecules are major causes that affect the reliability of existing and emerging biosensors. In this project our objective is to develop hybrid bio-CMOS techniques, that combines sensitivity of biological sensors with reliable computation on silicon to improve the performance of existing biosensor. As a model, we have chosen a disposable polyaniline and silicon based immunosensor that is relatively easy to fabricate and test. Our approach is to use source-channel coding principles to embed forward error-correction on the biosensor and decode the output produced by the sensor using a reliable silicon based processor.

• Details of fundamental building blocks for a polyaniline-based FEC biosensor can be found in the following Nanotechnology paper.
• Trace detection of pathogen using co-detection on a silicon-based FEC biosensor was reported in the Biosensors and Bioelectronics paper.
• Details of Femtoampere sensitivity potentiostat used in bioinstrumentation can be found in the TCAS-I paper.

Keyword: Biosensors, Forward error correction, reliability, nano-biosensors, pathogen detection, factor graph, hybrid systems, biomolecular circuits .

Sponsors: National Science Foundation

Auditory Processing

We are investigating real-time learning and signal processing algorithms that can be used for localization, separating and identifying acoustic targets of interest in the environment. This effort is divided into three parts: (a) microphone array and source localization; (b) noise-robust feature extraction in real-life environments and (c) classification and learning algorithms that can identify specific acoustic targets (speaker or keywords).

• Survey paper describing robust speaker verification techniques can be found here.
• Far-field acoustic source localization method can be found in this TCAS-I paper.

Keyword: Speech processing, noise-robust speech recognition, online learning, low-power filter-banks.

Sponsors: National Science Foundation, Johns Hopkins Applied Physics Laboratory

Machine Learning

We are investigating variants of support vector machine based learning algorithms and its extensions and fusion with other probabilistic models like hidden markov models and Gaussian mixture models.

• Gini-Support Vector Machine for multi-class classification and speaker verification (pdf).

Keyword: Support vector machines, Hidden Markov Models, online learning, Optimization.

Sponsors: Catalyst Foundation, New York

Questions and comments: shantanu at msu dot edu