Abstract

A method and system based on machine learning to create self-adapting analog circuits adapted to change their internal components on-the-fly in response to changes in process, voltage, and temperature to re-tune the electrical characteristics back to nominal specified values is disclose. The method and system herein comprise of designing the analog circuit, generating simulation data for machine learning, creating a full query database, creating and training, using simulation results, a machine learning (ML) model of the circuit and applying the ML model to infer the required changes to internal components of the analog circuit in response to changes in P, V, and T conditions. With this method and system, evaluation of the adverse effects of PVT changes, decision on internal circuit changes, and realization of requisite design changes are performed by the computer system solely within a ML data domain

Abstract

Low Quiescent Current, Capacitor-Less LDO with Adaptively Biased Power Transistors and Load Aware Feedback Resistance

Abstract

This paper presents a sub-1V, 593 pA current reference without using resistors and amplifiers. Temperature compensation is achieved by incorporating a CTAT gate to source voltage and a PTAT drain to source voltage in an NMOS-based sub-threshold biased circuit. The proposed architecture achieves process variation of ±0.75%, which enables seamless trim-free operation. Implemented in the 90nm CMOS process, the temperature-compensated current reference achieves a temperature coefficient of 378 ppm/°C over a high-temperature range of −40 to 100°C and a line sensitivity of 0.198%/V in a supply voltage range from 0.8 to 2.5 V. Duty cycling technique is adopted in the CTAT generator to reduce the total average power consumption. The proposed circuit occupies an area of 0.074 mm 2 while consuming only 3.3 nW at a typical corner of 27 °C and 0.8 V supply.

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

This paper introduces an ultra-low-power RC oscillator that utilizes a resistance amplification technique. The resistance amplifier achieves the effect of a large resistance by amplifying the I-V characteristics of a small resistance, resulting in significant area savings. To improve precision in oscillation frequency, a continuous time auto-zeroing technique is incorporated to dynamically calibrate the offset voltage of an integrated amplifier. Fabricated using a 180−nm CMOS process, the designed oscillator produces a 100−Hz clock signal while consuming only 2 nW power under compact silicon area (0.12mm2). Additionally, it achieves a competitive temperature coefficient (TC) of 280ppm/∘C and line sensitivity (LS) of 1.1 % / V without the need for any calibration.

Abstract

Skip to content IOP Science home Accessibility Help Search Journals Books Publishing Support Login Engineering Research Express Inclusive Publishing Trusted Science, find out more. Purpose-led Publishing, find out more. ACCEPTED MANUSCRIPT A label-free sensing of creatinine using radio frequency-driven lab-on-chip (loc) system Andleeb Zahra1, Swarnim Sinha2, Alimpan Modak3, Imran Siddiqui4, Azeemuddin Syed5, Prabhakar Bhimalapuram6, Tapan K. Sau7, Pawan Kumar8 and Zia Abbas9 Accepted Manuscript online 2 August 2024 • © 2024 IOP Publishing Ltd What is an Accepted Manuscript? DOI 10.1088/2631-8695/ad6ad5 DownloadAccepted Manuscript PDF Download PDF Article metrics 6 Total downloads Submit Submit to this Journal Permissions Get permission to re-use this article Share this article Article and author information Abstract This paper presents a promising avenue of Radio Frequency (RF) biosensors for sensitive and real-time monitoring of creatinine detection. Knowing creatinine levels in the human body is related to its possible association with renal, muscular, and thyroid dysfunction. The detection was performed using an Inter-Digitated Capacitor (IDC) made of copper (Cu) metal over an FR4 substrate. To demonstrate our methodology, we have chosen Phosphate Buffer (PB) as our solvent for making the creatinine solutions of different concentrations. Moreover, Assayed Chemistry Control (ACC), a reference control consisting of human serum-based solutions has been mixed with the different concentrations of creatinine in a ratio of 1:9 to spike the creatinine value in the ACC solution. The sensor has been designed using a High-Frequency Structure Simulator (HFSS) tool with an operating frequency of 2.53 GHz. Then the design is fabricated over the FR4 printed circuit board (PCB) and tested using a Vector Network Analyzer (VNA). However, the sensitive area of the IDC is introduced to grade 4 Whatman filter paper for the Sample Under Test (SUT) handling unit. The main advantage of using Whatman filter paper is that the uniform spreading of liquid reduces experimental error, and less volume is required for testing the sample. The principal idea implemented in the biosensor design is to track the shift in the operating frequency in the presence of different concentrations of creatinine mix in ACC solution with Phosphate Buffer (PB) solution as a reference.

Abstract

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees.