Compact low-power calibration mini-DACs for neural arrays with programmable weights

This paper considers the viability of compact low-resolution low-power mini digital-to-analog converters (mini-DACs) for use in large arrays of neural type cells, where programmable weights are required. Transistors are biased in weak inversion in order to yield small currents and low power consumptions, a necessity when building large size arrays. One important drawback of weak inversion operation is poor matching between transistors. The resulting effective precision of a fabricated array of 50 DACs turned out to be 47% (1.1 bits), due to transistor mismatch. However, it is possible to combine them two by two in order to build calibrated DACs, thus compensating for inter-DAC mismatch. It is shown experimentally that the precision can be improved easily by a factor of 10 (4.8% or 4.4 bits), which makes these DACs viable for low-resolution applications such as massive arrays of neural processing circuits. A design methodology is provided, and illustrated through examples, to obtain calibrated mini-DACs of a given target precision. As an example application, we show simulation results of using this technique to calibrate an array of digitally controlled integrate-and-fire neurons.Gobierno de España TIC1999-0446-C02-02, TIC2000-0406-P4-05, FIT-07000/2002/921, TIC2002-10878-EEuropean Union IST- 2001-3412

Low-Voltage Ultra-Low-Power Current Conveyor Based on Quasi-Floating Gate Transistors

The field of low-voltage low-power CMOS technology has grown rapidly in recent years; it is an essential prerequisite particularly for portable electronic equipment and implantable medical devices due to its influence on battery lifetime. Recently, significant improvements in implementing circuits working in the low-voltage low-power area have been achieved, but circuit designers face severe challenges when trying to improve or even maintain the circuit performance with reduced supply voltage. In this paper, a low-voltage ultra-low-power current conveyor second generation CCII based on quasi-floating gate transistors is presented. The proposed circuit operates at a very low supply voltage of only ±0.4 V with rail-to-rail voltage swing capability and a total quiescent power consumption of mere 9.5 µW. Further, the proposed circuit is not only able to process the AC signal as it's usual at quasi-floating gate transistors but also the DC which extends the applicability of the proposed circuit. In conclusion, an application example of the current-mode quadrature oscillator is presented. PSpice simulation results using the 0.18 µm TSMC CMOS technology are included to confirm the attractive properties of the proposed circuit

A Survey of Non-conventional Techniques for Low-voltage Low-power Analog Circuit Design

Designing integrated circuits able to work under low-voltage (LV) low-power (LP) condition is currently undergoing a very considerable boom. Reducing voltage supply and power consumption of integrated circuits is crucial factor since in general it ensures the device reliability, prevents overheating of the circuits and in particular prolongs the operation period for battery powered devices. Recently, non-conventional techniques i.e. bulk-driven (BD), floating-gate (FG) and quasi-floating-gate (QFG) techniques have been proposed as powerful ways to reduce the design complexity and push the voltage supply towards threshold voltage of the MOS transistors (MOST). Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application requirements. As an example of application three operational transconductance amplifiers (OTA) base on these non-conventional techniques are presented, the voltage supply is only ±0.4 V and the power consumption is 23.5 µW. PSpice simulation results using the 0.18 µm CMOS technology from TSMC are included to verify the design functionality and correspondence with theory

Biosensors and CMOS Interface Circuits

abstract: Analysing and measuring of biological or biochemical processes are of utmost importance for medical, biological and biotechnological applications. Point of care diagnostic system, composing of biosensors, have promising applications for providing cheap, accurate and portable diagnosis. Owing to these expanding medical applications and advances made by semiconductor industry biosensors have seen a tremendous growth in the past few decades. Also emergence of microfluidics and non-invasive biosensing applications are other marker propellers. Analyzing biological signals using transducers is difficult due to the challenges in interfacing an electronic system to the biological environment. Detection limit, detection time, dynamic range, specificity to the analyte, sensitivity and reliability of these devices are some of the challenges in developing and integrating these devices. Significant amount of research in the field of biosensors has been focused on improving the design, fabrication process and their integration with microfluidics to address these challenges. This work presents new techniques, design and systems to improve the interface between the electronic system and the biological environment. This dissertation uses CMOS circuit design to improve the reliability of these devices. Also this work addresses the challenges in designing the electronic system used for processing the output of the transducer, which converts biological signal into electronic signal.Dissertation/ThesisM.S. Electrical Engineering 201

An Analog VLSI Deep Machine Learning Implementation

Machine learning systems provide automated data processing and see a wide range of applications. Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog signal processing (ASP) can yield much higher energy efficiency than digital signal processing (DSP), presenting means of overcoming these limitations. The purpose of this work is to develop an analog implementation of DML system. First, an analog memory is proposed as an essential component of the learning systems. It uses the charge trapped on the floating gate to store analog value in a non-volatile way. The memory is compatible with standard digital CMOS process and allows random-accessible bi-directional updates without the need for on-chip charge pump or high voltage switch. Second, architecture and circuits are developed to realize an online k-means clustering algorithm in analog signal processing. It achieves automatic recognition of underlying data pattern and online extraction of data statistical parameters. This unsupervised learning system constitutes the computation node in the deep machine learning hierarchy. Third, a 3-layer, 7-node analog deep machine learning engine is designed featuring online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes massively parallel reconfigurable current-mode analog architecture to realize efficient computation. And algorithm-level feedback is leveraged to provide robustness to circuit imperfections in analog signal processing. At a processing speed of 8300 input vectors per second, it achieves 1×1012 operation per second per Watt of peak energy efficiency. In addition, an ultra-low-power tunable bump circuit is presented to provide similarity measures in analog signal processing. It incorporates a novel wide-input-range tunable pseudo-differential transconductor. The circuit demonstrates tunability of bump center, width and height with a power consumption significantly lower than previous works

A digital tuning scheme for digitally programmable integrated continuous-time filters and techniques for high-precision monolithic linear circuit design and implementation

Multiple topics which all focus on precision monolithic circuit design but beyond this are not directly related to each other are presented. The first topic is a digital tuning scheme for digitally programmable integrated continuous-time filters (4), (8) - (10). Emphasis of this research is on development of a more general tuning scheme which can be applicable to various filter functions as well as high-frequency applications. The tuning scheme consists of two phases: system identification and adjustment. Various continuous-time filter identification methods including time-domain and frequency-domain approaches are investigated, and a filter adjustment algorithm is presented. Potential of high accuracy of the proposed tuning scheme and successful applicability to high-frequency filters with versatile functions have been demonstrated through simulations and experiments;Four other topics are separately presented. First, nonidealities associated with high-precision amplifiers (5), (7) are discussed. Special emphasis is given on analysis of statistical characteristics of random CMRR and offset of CMOS op-amps which can help estimating yield of high-volume production and help engineers design for a given yield. Next, an automatic offset compensation scheme for CMOS op-amps with ping-pong control (2), (6) is presented. A very low-voltage circuit design technique using floating gate MOSFETs (3) is introduced. Finally, an accurate and matching-free threshold voltage extraction scheme using a ratio-independent SC amplifier and a dynamic current mirror (1) is discussed

Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications

Advanced correlation filters have been employed in a wide variety of image processing and pattern recognition applications such as automatic target recognition and biometric recognition. Among those, object recognition and tracking have received more attention recently due to their wide range of applications such as autonomous cars, automated surveillance, human-computer interaction, and vehicle navigation.Although digital signal processing has long been used to realize such computational systems, they consume extensive silicon area and power. In fact, computational tasks that require low to moderate signal-to-noise ratios are more efficiently realized in analog than digital. However, analog signal processing has its own caveats. Mainly, noise and offset accumulation which degrades the accuracy, and lack of a scalable and standard input/output interface capable of managing a large number of analog data.Two digitally-interfaced analog correlation filter systems are proposed. While digital interfacing provided a standard and scalable way of communication with pre- and post-processing blocks without undermining the energy efficiency of the system, the multiply-accumulate operations were performed in analog. Moreover, non-volatile floating-gate memories are utilized as storage for coefficients. The proposed systems incorporate techniques to reduce the effects of analog circuit imperfections.The first system implements a 24x57 Gilbert-multiplier-based correlation filter. The I/O interface is implemented with low-power D/A and A/D converters and a correlated double sampling technique is implemented to reduce offset and lowfrequency noise at the output of analog array. The prototype chip occupies an area of 3.23mm2 and demonstrates a 25.2pJ/MAC energy-efficiency at 11.3 kVec/s and 3.2% RMSE.The second system realizes a 24x41 PWM-based correlation filter. Benefiting from a time-domain approach to multiplication, this system eliminates the need for explicit D/A and A/D converters. Careful utilization of clock and available hardware resources in the digital I/O interface, along with application of power management techniques has significantly reduced the circuit complexity and energy consumption of the system. Additionally, programmable transconductance amplifiers are incorporated at the output of the analog array for offset and gain error calibration. The prototype system occupies an area of 0.98mm2 and is expected to achieve an outstanding energy-efficiency of 3.6pJ/MAC at 319kVec/s with 0.28% RMSE