Arjang Hassibi

A CMOS Electrochemical Impedance Spectroscopy (EIS) Biosensor Array

In this paper, we present a fully integrated biosensor 10 × 10 array in a standard complementary metal-oxide semiconducor process, which takes advantage of electrochemical impedance spectroscopy (EIS). We also show that this system is able to detect various biological analytes, such as DNA and proteins, in real time and without the need for molecular labels. In each pixel of this array, we implement a biocompatible Au electrode transducer and embedded sensor circuitry which takes advantage of the coherent detector to measure the impedance of the associated electrode-electrolyte interface. This chip is capable of concurrently measuring admittance values as small as 10-8 Ω-1 within the array with the detection dynamic range of more than 90 dB in the frequency range of 10 Hz-50 MHz.

Comprehensive study of noise processes in electrode electrolyte interfaces

A general circuit model is derived for the electrical noise of electrode–electrolyte systems, with emphasis on its implications for electrochemical sensors. The noise power spectral densities associated with all noise sources introduced in the model are also analytically calculated. Current and voltage fluctuations in typical electrode–electrolyte systems are demonstrated to originate from either thermal equilibrium noise created by conductors, or nonequilibrium excess noise caused by charge transfer processes produced by electrochemical interactions. The power spectral density of the thermal equilibrium noise is predicted using the fluctuation-dissipation theorem of thermodynamics, while the excess noise is assessed in view of charge transfer kinetics, along with mass transfer processes in the electrode proximity. The presented noise model not only explains previously reported noise spectral densities such as thermal noise in sensing electrodes, shot noise in electrochemical batteries, and 1/f noise in corrosive interfaces, it also provides design-oriented insight into the fabrication of low-noise micro- and nanoelectrochemical sensors.

Delay-Line-Based Analog-to-Digital Converters

We will introduce a design of analog-to-digital converters (ADCs) based on digital delay lines. Instead of voltage comparators, they convert the input voltage into a digital code by delay lines and are mainly built on digital blocks. This makes it compatible with process scaling. Two structures are proposed, and tradeoffs in the design are discussed. The effects of jitter and mismatch are also studied. We will present two 4 bit, 1 GS/s prototypes in 0.13 mum and 65 nm CMOS processes, which show a small area (0.015 mm2) and small power consumption (<2.4 mW).

A Programmable 0.18-

A configurable electrochemical sensor microarray system-on-a-chip fabricated in a standard digital 0.18-mum complementary metal-oxide-semiconductor (CMOS) process is presented. Each pixel within this 5times10 array occupies a 160 mumtimes120 mum area and contains a differential electrochemical transducer with a programmable sensor. The sensor has a digitally configurable topology capable of performing different electroanalytical measurements for a variety of affinity-based biomolecular sensing applications. The main modes of operation for this system are impedance spectroscopy, voltammetry, potentiometry, and field-effect sensing

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Future Point-of-Care (PoC) molecular-level diagnosis requires advanced biosensing systems that can achieve high sensitivity and portability at low power consumption levels, all within a low price-tag for a variety of applications such as in-field medical diagnostics, epidemic disease control, biohazard detection, and forensic analysis. Magnetically labeled biosensors are proposed as a promising candidate to potentially eliminate or augment the optical instruments used by conventional fluorescence-based sensors. However, magnetic biosensors developed thus far require externally generated magnetic biasing fields [1–4] and/or exotic post-fabrication processes [1,2]. This limits the ultimate form-factor of the system, total power consumption, and cost. To address these impediments, we present a low-power scalable frequency-shift magnetic particle biosensor array in bulk CMOS, which provides single-bead detection sensitivity without any (electrical or permanent) external magnets.

CMOS Electrochemical Sensor Microarray for Biomolecular Detection

As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution-from circuit to compiler-that enables general-purpose use of limited-precision, analog hardware to accelerate “approximable” code-code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a von Neumann model to an “analog” neural model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neural-network training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7× and energy savings of 6.3× with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range of approximation-tolerant, emerging applications including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing.

A frequency-shift CMOS magnetic biosensor array with single-bead sensitivity and no external magnet

In this paper, we present a comprehensive stochastic model describing the measurement uncertainty, output signal, and limits of detection of affinity-based biosensors. The biochemical events within the biosensor platform are modeled by a Markov stochastic process, describing both the probabilistic mass transfer and the interactions of analytes with the capturing probes. To generalize this model and incorporate the detection process, we add noisy signal transduction and amplification stages to the Markov model. Using this approach, we are able to evaluate not only the output signal and the statistics of its fluctuation but also the noise contributions of each stage within the biosensor platform. Furthermore, we apply our formulations to define the signal-to-noise ratio, noise figure, and detection dynamic range of affinity-based biosensors. Motivated by the platforms encountered in practice, we construct the noise model of a number of widely used systems. The results of this study show that our formulations predict the behavioral characteristics of affinity-based biosensors which indicate the validity of the model.

General-purpose code acceleration with limited-precision analog computation

We study the statistical behavior of affinity-based biosensors. The detection uncertainty and noise in such devices originates primarily from probabilistic molecular-level bindings within the sensing regions, and the stochastic mass-transfer processes within the reaction chamber. In this paper, we model the dynamic behavior of these sensory systems by a Markov process, which enables us to estimate the sensor inherent noise power spectral density (PSD) and response time. We also present the methods by which the Markov parameters are extracted from the reaction kinetic rates, diffusion coefficients, and reaction chamber boundary conditions. Using this model, we explain why Poisson shot noise has been reported in such biosensors and additionally predict a Lorentzian profile for the fluctuation PSD. Furthermore, we demonstrate that affinity-based biosensors have a quantum-limited signal-to-noise ratio (SNR). We also show that the SNR decreases as the dimensions are isomorphically scaled down while the biosens...

On noise processes and limits of performance in biosensors

Biosensors are one of the fundamental detection platforms in biotechnology. They take advantage of unique biomolecular interactions to capture and detect specific analytes on a surface. The detection versatility of biosensors has always been their key advantage and it has been demonstrated that they can detect almost any analyte such as DNA, proteins, metabolites, and even micro-organisms. However, the achievable SNR and detection DR of biosensors can be very low. This is due to the fact that the capturing processes in biosensors suffer from a significant amount of biological interference (i.e., non-specific bindings) and biochemical noise which typically necessitate the use of complex biochemical labeling processes and sophisticated detectors [1]. Hence, the main design challenge of biosensors is to increase the SNR and DR while minimizing the complexity of both the assay and the detector. Today, this is the main impediment in point-of-care (PoC) biosensors, particularly in high-performance applications such as molecular diagnostics and forensics.

Biological shot-noise and quantum-limited signal-to-noise ratio in affinity-based biosensors

An energy-autonomous and MRI-compatible CMOS implantable sensor is presented that operates by harvesting the energy of the light which penetrates into the tissue. On-chip P+/N-well diodes are used as on-chip photovoltaic cells and in-vivo physiological data is transmitted neuromorphically to the skin surface.