Presents a collection of slides that covers the following topics: CMOS biochips for point-of-care medical molecular diagnostics; diagrams of diagnostics information flows; molecular diagnostics and structural analysis; a comparative financial analysis of current state-of-the-art diagnostic technologies, i.e., PCR, DNA Arrays, DNA Sequencing, etc.; CMLS biochip designs; biosensing pixels; manufacturing facilities; the HYDRA-1K platform; and chip performance characterisation.

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.

We report an on-chip temperature sensor that uses the temperature-dependent reverse bias leakage current of a lateral SOI-CMOS pn diode to measure the thermal profile of a 32-nm microprocessor core. In this system, the diode junction capacitance is first charged to a fixed voltage. Subsequently, the diode capacitance is allowed to self-discharge by its reverse bias leakage current to create a temperature-dependent time pulse whose width is measured by a digital counter. This sensor demonstrates a 3s measurement inaccuracy of ±1.95 °C across the 5-100 °C temperature range while consuming 100 μW from a single 1.65 V supply.

Estimation of the DNA copy number in a given biological sample is an important problem in genomics. Quantitative polymerase chain reaction (qPCR) systems detect the target DNA molecules by amplifying their number through a series of thermal cycles and measuring the amount of created amplicons in each cycle. Ideally, the number of target molecules doubles at the end of each cycle. However, in practice, due to biochemical noise the efficiency of the qPCR reaction - defined as the fraction of the target molecules which are successfully copied during a cycle - is always less than1. In this paper, we formulate the problem of the joint maximum-likelihood estimation of the qPCR efficiency and the initial DNA copy number. Then, we analytically determine the limits of performance of qPCR by deriving the Cramer-Rao lower bound on the mean-square estimation error. As indicated by simulation studies, the performance of the proposed estimator is superior compared to competing statistical approaches. The proposed approach is validated using experimental data.

In this paper we introduce a novel Analog-to-Digital architecture for high speed applications that is compatible with digital CMOS and surpasses the issues with traditional voltage conversion techniques. The quantization method is based on the delay-to-digital concept as a means to quantize a variable delay line. A 4 bit 1 GS/s ADC with 1 mW power consumption is designed in 65 nm CMOS based on the proposed architecture. The new architecture is highly scalable with CMOS technology and because of its delay-line-based core, the ADCs performance enhances with further CMOS scaling and provides a promising method for the trend toward more digital implementation of circuits.

Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady-state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed in (Hassibi, 2007). The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In the current paper, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross-hybridization.

Estimation of DNA copy number in a given biological sample is an extremely important problem in genomics. This problem is especially challenging when the number of the DNA strands is minuscule, which is often the case in applications such as pathogen and genetic mutation detection. A recently developed technique, real-time polymerase chain reaction (PCR), amplifies the number of initial target molecules by replicating them through a series of thermal cycles. Ideally, the number of target molecules doubles at the end of each cycle. However, in practice, due to biochemical noise the efficiency of the PCR reaction, defined as the fraction of target molecules which are successfully copied during a cycle, is always less than 1. In this paper, we formulate the problem of joint maximum-likelihood estimation of the PCR efficiency and the initial DNA copy number. As indicated by simulation studies, the performance of the proposed estimator is superior with respect to competing statistical approaches. Moreover, we compute the Cramer-Rao lower bound on the mean-square estimation error.

This paper presents novel on-chip antenna structures and provides new on-chip circuit concepts that may be implemented as part of standard integrated circuit fabrication processes for wireless integrated circuits of the future. As wavelengths of wireless local area networks (WLANs) and personal area networks (PANs) shrink to millimeter lengths at 60 GHz and above, interconnectivity issues and cost/scale requirements will require these new approaches for built-in on- chip antennas. This paper highlights advancements in both the integrated circuits and microwave technology research communities, and presents a number of promising areas of research. Finally, we describe an experimental probe station that has been developed at The University of Texas for the testing and characterization of novel on-chip antennas and distributed components.

DNA microarray technology relies on the hybridization process which is stochastic in nature. Probabilistic cross-hybridization of non-specific targets, as well as the shot-noise originating from specific targets binding, are among the many obstacles for achieving high accuracy in DNA microarray analysis. In this paper, we use statistical model of hybridization and cross-hybridization processes to derive a lower bound (viz., the Cramer-Rao bound) on the minimum mean-square error of the target concentrations estimation. A preliminary study of the Cramer-Rao bound for estimating the target concentrations suggests that, in some regimes, cross-hybridization may, in fact, be beneficial - a result with potential ramifications for probe design, which is currently focused on minimizing cross-hybridization