A CMOS four-quadrant analog multiplier

A new circuit configuration for an MOS four-quadrant analog multiplier circuit is presented. It is based on the square-law characteristics of the MOS transistor. Two versions have been realized. The first has a linearity better than 0.14 percent for an output current swing of 36 percent of the supply current and a bandwidth from dc to 1 MHz. The second version has floating inputs, a linearity of 0.4 percent at an output current swing of 40 percent of the supply current and a bandwidth from dc to above 4.5 MHz

A class of analog CMOS circuits based on the square-law characteristic of an MOS transistor in saturation

The examined class of circuits includes voltage multipliers, current multipliers, linear V-I convertors, linear I-V convertors, current squaring circuits, and current divider circuits. Typical for these circuits is an independent control of the sum as well as the difference between two gate-source voltages. As direct use is made of the basic device characteristics, only a small number of transistors is required in the presented circuits

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

Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS

In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits. The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis. To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis. Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.Siirretty Doriast

Developing large-scale field-programmable analog arrays for rapid prototyping

Field-programmable analog arrays (FPAAs) provide a method for rapidly prototyping analog systems. While currently available FPAAs vary in architecture and interconnect design, they are often limited in size and flexibility. For FPAAs to be as useful and marketable as modern digital reconfigurable devices, new technologies must be explored to provide area efficient, accurately programmable analog circuitry that can be easily integrated into a larger digital/mixed signal system. By leveraging recent advances in floating gate transistors, a new generation of FPAAs are achievable that will dramatically advance the current state of the art in terms of size, functionality, and flexibility

Low-Power Analog Circuits for Sub-Band Speech Processing

The need for efficient electronics has been increasing by the day, as have the constraints on power and size of the devices. Also the increase in use of mobile and wearable electronics has been leading to innovative methods to conserve power and increase functionality. The traditional approach of signal processing heavily relies on the Digital Signal Processing (DSP) hardware to perform most of the tasks, which has lead to power-hungry circuits. Use of analog front-end devices could prove to be efficient, since most of the real-world data is analog and since the DSP could be spared for more application-specific tasks within the system, thereby resulting in more efficient mixed-signal systems.;The focus in this work is to develop an analog front-end for speech-processing applications with inspiration from biology, and trying to mimic human auditory perception techniques. The circuits are designed in 600nm, 350nm and 180nm CMOS processes and are biased in the sub-threshold region to consume low-power. Also, various modules of the system are connected using multiplexing circuits to allow post-fabrication reconfigurability to suit various applications. These circuits are biased using a network of floating-gate transistors which allow reconfigurability and increased bias accuracy. This thesis mainly describes two modules of the analog front-end used for speech processing: derivative circuit and voltage-mode subtractor circuit, which are used for processing spectrally decomposed signals. These circuits could be used for applications like audio analysis or event detection

Dynamic charge restoration of floating gate subthreshold MOS translinear circuits

We extend a class of analog CMOS circuits that can be used to perform many analog computational tasks. The circuits utilize MOSFET's in their subthreshold region as well as capacitors and switches to produce the computations. We show a few basic current-mode building blocks that perform squaring, square root, and multiplication/division which should be sufficient to gain an understanding of how to implement other power law circuits. We then combine the circuit building blocks into a more complicated circuit that normalizes a current by the square root of the sum of the squares (vector sum) of the currents. Each of these circuits have switches at the inputs of their floating gates which are used to dynamically set and restore the charges at the floating gates to proceed with the computation

First order sigma-delta modulator of an oversampling ADC design in CMOS using floating gate MOSFETS

We report a new architecture for a sigma-delta oversampling analog-to-digital converter (ADC) in which the first order modulator is realized using the floating gate MOSFETs at the input stage of an integrator and the comparator. The first order modulator is designed using an 8 MHz sampling clock frequency and implemented in a standard 1.5µm n-well CMOS process. The decimator is an off-chip sinc-filter and is programmed using the VERILOG and tested with Altera Flex EPF10K70RC240 FPGA board. The ADC gives an 8-bit resolution with a 65 kHz bandwidth