Analog-digital and digital-analog converters using single-electron and MOS transistors

This paper proposes two kinds of novel single-electron analog-digital conversion (ADC) and digital-analog conversion (DAC) circuits that consist of single-electron transistors (SETs) and metal-oxide-semiconductor (MOS) transistors. The SET/MOS hybrid ADC and DAC circuits possess the merits of the SET circuit and the MOS circuit. We obtain the SPICE macro-modeling code of the SET transistor by studying and fitting the characteristics of the SET with SPICE simulation and Monte Carlo simulation methods. The SPICE macro-modeling code is used for the simulation of the SET/MOS hybrid ADC and DAC circuits. We simulate the performances of the SET/MOS hybrid 3-b ADC and 2-b DAC circuits by using the H-SPICE simulator. The simulation results demonstrate that the hybrid circuits can perform analog-digital and digital-analog data conversion well at room temperature. The hybrid ADC and DAC circuits have advantages as-follows: 1) compared with conventional circuits, the architectures of the circuits are simpler; 2) compared with single electron transistor circuits, the circuits have much larger load capability; 3) the power dissipation of the circuits are lower than uW; 4) the data conversion rate of the circuits can exceed 100 MHz; and 5) the resolution of the ADC and DAC circuits can be increased by the pipeline architectures.

Analog computation using quantum-dot cell network

A novel analog-computation system using a quantum-dot cell network is proposed to solve complex problems. Analog computation is a promising method for solving a mathematical problem by using a physical system analogous to the problem. We designed a novel quantum-dot cell consisting of three-stacked. quantum dots and constructed a cell network utilizing the nearest-neighbor interactions between the cells. We then mapped a graph 3-colorability problem onto the network so that the single-electron configuration of the network in the ground state corresponded to one of the solutions. We calculated the ground state of the cell network and found solutions to the problems. The results demonstrate that analog computation is a promising approach for solving complex problems.

The design of cascaded resistors in a new analog switch two-step ADC architecture

In this paper we introduce a new Half-flash analog switch ADC architecture. And we discuss two methods to design the values of the cascaded resistors which generate the reference voltages. Derailed analysis about the effect of analog switches and comparators on reference voltages, and the methods to set the resistor values and correspond;ng voltage errors are given.

An analysis of the performance of a new high-speed single-way analog switch

This paper introduces a new highspeed single-way analog switch which has both highspeed high-resolution mono-direction analog transmission gate function and high-speed digital logic gate function with normal bipolar technology. The analysis of static and transient switching performances as an analog transmission gate is emphasized in the paper. In order to reduce the plug-in effect on high-speed high-resolution systems, an optimum design scheme is also given. This scheme is to achieve accelerated dynamic response with very low bias power dissipation. The analysis of PSPICE simulation as well as the circuit test results confirms the feasibility of the scheme. Now, the circuit has been applied effectively to the designs of novel highspeed A/D and D/A converters.

A one-way MOS analog switch

In this paper, a one-way NMOS analog switch featuring a low plug-in consumption is presented. The performances of analog switch, especially the performances of source follower are simulated under different conditions with PSPICE. Simulation results and factors affecting the deviation between input and output are analyzed, some advice on how to reduce the deviation between input and output is given. Ar the end of the paper, voltage relationship between input and output of the analog switch is obtained. Function of first degree, Vout = kVin + V0, is used to approximate the voltage relationship. The simulation results anti the value achieved from the approximation equation are given as well.

Analog computation using quantum-dot cell network

A novel analog-computation system using a quantum-dot cell network is proposed to solve complex problems. Analog computation is a promising method for solving a mathematical problem by using a physical system analogous to the problem. We designed a novel quantum-dot cell consisting of three-stacked. quantum dots and constructed a cell network utilizing the nearest-neighbor interactions between the cells. We then mapped a graph 3-colorability problem onto the network so that the single-electron configuration of the network in the ground state corresponded to one of the solutions. We calculated the ground state of the cell network and found solutions to the problems. The results demonstrate that analog computation is a promising approach for solving complex problems.

An Analog VLSI Chip for Estimating the Focus of Expansion

For applications involving the control of moving vehicles, the recovery of relative motion between a camera and its environment is of high utility. This thesis describes the design and testing of a real-time analog VLSI chip which estimates the focus of expansion (FOE) from measured time-varying images. Our approach assumes a camera moving through a fixed world with translational velocity; the FOE is the projection of the translation vector onto the image plane. This location is the point towards which the camera is moving, and other points appear to be expanding outward from. By way of the camera imaging parameters, the location of the FOE gives the direction of 3-D translation. The algorithm we use for estimating the FOE minimizes the sum of squares of the differences at every pixel between the observed time variation of brightness and the predicted variation given the assumed position of the FOE. This minimization is not straightforward, because the relationship between the brightness derivatives depends on the unknown distance to the surface being imaged. However, image points where brightness is instantaneously constant play a critical role. Ideally, the FOE would be at the intersection of the tangents to the iso-brightness contours at these "stationary" points. In practice, brightness derivatives are hard to estimate accurately given that the image is quite noisy. Reliable results can nevertheless be obtained if the image contains many stationary points and the point is found that minimizes the sum of squares of the perpendicular distances from the tangents at the stationary points. The FOE chip calculates the gradient of this least-squares minimization sum, and the estimation is performed by closing a feedback loop around it. The chip has been implemented using an embedded CCD imager for image acquisition and a row-parallel processing scheme. A 64 x 64 version was fabricated in a 2um CCD/ BiCMOS process through MOSIS with a design goal of 200 mW of on-chip power, a top frame rate of 1000 frames/second, and a basic accuracy of 5%. A complete experimental system which estimates the FOE in real time using real motion and image scenes is demonstrated.

Computing 3-D Motion in Custom Analog and Digital VLSI

This thesis examines a complete design framework for a real-time, autonomous system with specialized VLSI hardware for computing 3-D camera motion. In the proposed architecture, the first step is to determine point correspondences between two images. Two processors, a CCD array edge detector and a mixed analog/digital binary block correlator, are proposed for this task. The report is divided into three parts. Part I covers the algorithmic analysis; part II describes the design and test of a 32$\time $32 CCD edge detector fabricated through MOSIS; and part III compares the design of the mixed analog/digital correlator to a fully digital implementation.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

Functional desensitization of the isolated beta-adrenergic receptor by the beta-adrenergic receptor kinase: potential role of an analog of the retinal protein arrestin (48-kDa protein).

The beta-adrenergic receptor kinase is an enzyme, possibly analogous to rhodopsin kinase, that multiply phosphorylates the beta-adrenergic receptor only when it is occupied by stimulatory agonists. Since this kinase may play an important role in mediating the process of homologous, or agonist-specific, desensitization, we investigated the functional consequences of receptor phosphorylation by the kinase and possible analogies with the mechanism of action of rhodopsin kinase. Pure hamster lung beta 2-adrenergic receptor, reconstituted in phospholipid vesicles, was assessed for its ability to mediate agonist-promoted stimulation of the GTPase activity of coreconstituted stimulatory guanine nucleotide-binding regulatory protein. When the receptor was phosphorylated by partially (approximately 350-fold) purified preparations of beta-adrenergic receptor kinase, as much as 80% inactivation of its functional activity was observed. However, the use of more highly purified enzyme preparations led to a dramatic decrease in the ability of phosphorylation to inactivate the receptor such that pure enzyme preparations (approximately 20,000-fold purified) caused only minimal (approximately 1off/- 7%) inactivation. Addition of pure retinal arrestin (48-kDa protein or S antigen), which is involved in enhancing the inactivating effect of rhodopsin phosphorylation by rhodopsin kinase, led to partial restoration of the functional effect of beta-adrenergic receptor kinase-promoted phosphorylation (41 +/- 3% inactivation). These results suggest the possibility that a protein analogous to retinal arrestin may exist in other tissues and function in concert with beta-adrenergic receptor kinase to regulate the activity of adenylate cyclase-coupled receptors.

A dose-finding study of lanreotide (a somatostatin analog) in patients with colorectal carcinoma

BACKGROUND. Laboratory data suggest that insulin-like growth factor-1 (IGE-1) may stimulate the growth of different human tumors. At least in acromegalic patients, somatostatin (SMS) analogs, such as lanreotide, suppress the serum levels of growth hormone (GH) and IGE-1. METHODS. To evaluate the tolerability and biologic activity of different doses of lanreotide in patients with advanced colorectal carcinoma, consecutive groups of 3 patients each were subcutaneous treated with lanreotide at doses of 1, 2, 3, 4, 5, or 6 mg three times a day for 2 months. In the event of Grade 3 side effects, 3 additional patients were treated with the same dose before the next dose escalation. Serum samples were obtained on Days 0, 15, 30, and 60 for serum GH, IGF-1, and lanreotide assessment. RESULTS. Twenty-four patients were enrolled and all were evaluable. Except for the 3 and 6 mg doses, for which the observation of a Grade 3 side effect required that an additional three patients be treated, it was sufficient to treat 3 patients at each dose. The overall incidence of side effects was as follows: changes in bowel habits, 83%; abdominal cramps, 79%; diarrhea, 17%; vomiting, 17%; nausea, 21%; steatorrhea, 78%; hyperglycemia, 35%; laboratory hypothyroidism, 39%; gallstones, 13%; and weight loss, 17%. No evidence of an increase in the incidence, intensity, or duration of side effects was observed with dose escalation. Serum IGF-1 levels were as follows: Day 13: 63%, 60%, and 67% of the baseline values for the low (12 mg), intermediate (3-4 mg), and high (5- 6 mg) dose groups, respectively; Day 30: 63%, 59%, and 51%, respectively; and Day 60: 73%, 69%, and 47%, respectively. Serum lanreotide levels declined during treatment in all of the dose groups (90 ng/mL on Day 15, and 35 ng/mL on Day 60 for the 5-6 mg group; 10 ng/mL on Day 15, and 1.5 ng/mL on Day 60 for the 1-2 mg group). No antitumor activity or tumor marker reduction was observed. CONCLUSIONS. No increase in toxicity was observed when subcutaneous lanreotide doses were escalated to 6 mg three times a day for 2 months. The highest doses seemed to maintain reduced serum IGF-1 levels; with the lowest doses, a 'rebound' in serum IGF-1 levels was observed during treatment. Nevertheless, intermittent subcutaneous injections do not ensure constant serum drug concentrations over time.