Abstract

The prevalence of MOSFETs in diverse electronic applications highlights their significance. However, the sensitivity of MOSFET threshold voltage to process variation poses a substantial challenge in achieving consistent device performance. This paper proposes a novel approach to overcome this challenge by employing a threshold voltage (VTH) tracking circuit that tracks the variation in VTH. Our primary objective is to develop a current reference that remains invariant across process, voltage, and temperature (PVT) variations, eliminating the need for an external trimming circuit. This is achieved by the meticulous integration of VTH tracking circuit, CTAT generator, PTAT generator, and a current adder. By employing this design,a process variation (σ/µ) of 0.48%, a temperature coefficient (TC) of 210ppm/ ◦C for a temperature range of −40◦C to 120◦C, and a line sensitivity of 1.6% is achieved over a voltage range of 1.6V − 3V. The proposed circuit occupies an area of 0.013mm2 while consuming power of 56µW at a typical corner of 27◦C and a supply of 1.8V. The given circuit is implemented in a 180nm CMOS process node with a generated reference current of 61nA.

Abstract

This paper presents a new approach to efficient process monitor (PMON) design. It focuses on robust process tracking of PMOS and NMOS devices while minimizing the effects of external factors such as die temperature variations and local supply voltage changes. The proposed design method introduces a comprehensive set of PMON libraries tailored for the specific device types provided by a target technology node. Central to this method is a novel design automation routine based on Zero Temperature Coefficient (ZTC) biasing of a ring oscillator-based PMON. This work demonstrates the effectiveness and scalability of the proposed method across multiple process nodes. The generated PMON structures show a multi-fold improvement in sensitivity for process tracking compared to current state-of-the-art solutions.

Abstract

This paper presents a low-voltage, low-power PVT-invariant voltage reference with excellent line sensitivity for IoT and biomedical applications. By applying a bias current in an NMOS-based composite pair and bias voltage at the body of NMOS to get the temperature-compensated voltage reference over a wide temperature range. The proposed design implemented in a 180nm CMOS process gives an output of 141mV which is independent of process, voltage, and temperature. Without trimming, the process variation of the proposed design is 0.988% , and the temperature coefficient of the proposed voltage reference is over a wide temperature range of to . For a supply voltage ranging from 0.6V the line sensitivity of the reference is 0.0012%N.

Abstract

Designing robust performance models for modern complex digital circuits in the face of rapidly accelerating process variations is a critical yet demanding task. This paper introduces an efficient statistical performance modeling approach for VLSI digital circuits that incurs minimal computational expense. The fundamental concept involves capitalizing on knowledge gained from circuit modeling in one technology node to streamline the modeling process in another. This is achieved by merging previously established statistical models of process technology with a limited set of simulation data from a subsequent process technology through transfer learning. Comprehensive experiments conducted across diverse technology nodes demonstrate that the proposed framework is robust, precise, efficient in data usage, and computationally superior to other cutting-edge performance modeling techniques.

Abstract

Polynomial dynamical systems (i.e. dynamical systems with polynomial right hand side) are ubiquitous in applications, especially as models of reaction networks and interaction networks. The properties of general polynomial dynamical systems can be very difficult to analyze, due to nonlinearity, bifurcations, and the possibility for chaotic dynamics. On the other hand, toric dynamical systems are polynomial dynamical systems that appear naturally as models of reaction networks, and have very robust and stable properties. A disguised toric dynamical system is a polynomial dynamical system generated by a reaction network N and some choice of positive parameters, such that (even though it may not be toric with respect to N) it has a toric realization with respect to some network N′. Disguised toric dynamical systems enjoy all the robust stability properties of toric dynamical systems. In this paper, we study a larger set of dynamical systems where the rate constants are allowed to take both positive and negative values. More precisely, we analyze the R-disguised toric locus of a reaction network N, i.e., the subset in the space rate constants (positive or negative) of N for which the corresponding polynomial dynamical system is disguised toric. We focus especially on finding a lower bound on the dimension of the R-disguised toric locus.

Abstract

In this article we present a benchmark for resource characterization in the process of controlled quantum state reconstruction and secret sharing for general three-qubit states. This is achieved by providing a closed expression for the reconstruction fidelity, which relies on the genuine tripartite correlation and the bipartite channel between the dealer and the reconstructor characterized by the respective correlation parameters. We formulate the idea of quantum advantage in approximate state reconstruction as surpassing the classical limit set at 2/3. This article introduces new interoperability between teleportation and state reconstruction. This is detailed through a case-by-case analysis of relevant correlation matrices. We reformulate the idea of quantum secret sharing by setting up additional constraints on the teleportation capacity of the bipartite channels between dealer and shareholders by ensuring that, individually, the shareholders cannot reconstruct the secret. We believe that this will give us an ideal picture of how quantum secret sharing should be.

Abstract

Visual servoing for the development of autonomous robotic systems capable of administering UltraSound (US) guided regional anesthesia requires real-time segmentation of nerves, needle tip localization and needle trajectory extrapolation. First, we recruited 227 patients to build a large dataset of 41,000 anesthesiologist annotated images from US videos of brachial plexus nerves and developed models to localize nerves in the US images. Generalizability of the best suited model was tested on the datasets constructed from separate US scanners. Using these nerve segmentation predictions, we define automated anesthesia needle targets by fitting an ellipse to the nerve contours. Next, we developed an image analysis tool to guide the needle toward their targets. For the segmentation of the needle, a natural RGB pre-trained neural network was first fine-tuned on a large US dataset for domain transfer and then adapted for the needle using a small dataset. The segmented needle’s trajectory angle is calculated using Radon transformation and the trajectory is extrapolated from the needle tip. The intersection of the extrapolated trajectory with the needle target guides the needle navigation for drug delivery. The needle trajectory’s average error was within acceptable range of 5 mm as per experienced anesthesiologists. The entire dataset has been released publicly for further study by the research community at https://github.com/Regional-US/

Abstract

We present CheXtriev, a graph-based, anatomy-aware frame- work for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract in- formative features from specific anatomical regions. Furthermore, it cap- tures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accu- rate, effective and efficient retrieval, particularly for less prevalent find- ings. CheXtriv outperforms state-of-the-art global and local approaches by 18% to 26% in retrieval accuracy and 11% to 23% in ranking quality. The code is available at https://github.com/cvit-mip/chextriev.

Abstract

Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.

Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.