Italy

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9194762

**Abstract:**

In this paper a stitched array of 768x64 pixels for X-ray dental imaging is presented. The sensor architecture allows to increase the frame rate up to 900 fps thanks to a pipeline approach that minimizes the deadtime between consecutive frames. A column-based ADC converts pixel values in digital form while an external FPGA performs the operation of TDI accumulation, thus increasing the SNR. Internal programmable gain allows to achieve a maximum sensitivity of 2.6 LSB/nGy and, at the same time, to extend the dynamic range of the sensor in case of high intensity. The final system consists of two sensors each one having a size of 74.5x9 mm, assembled in a PCB and coupled with a high sensitive columnar thallium-doped cesium iodide (CsI(Tl)) scintillator for X-ray conversion. The sensor, fabricated in a standard CMOS 0.15um technology with stitching approach, shows a pixel size of 97 um

ESSCIRC ESSDERC 2021

Luximos: a 768x64 900-fps Tileable Pipelined X-Ray CMOS Image Sensor for Dental Imaging with 2.6 LSB/nGy Sensitivity

image sensor

time delay integration

dental imaging

x-ray

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

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To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.

Highlights:
* 3 joint keynote presentations
* 3 keynote presentations ESSCIRC
* 3 keynote presentations ESSDERC
* Invited papers with overall coverage of all aspects of advanced devices and circuits
* Presentation of IEEE and ESSCIRC/ESSDERC Awards
* Educational Events

JOINT PLENARY talks live broadcast: September 13, 2021 
ESSCIRC and ESSDERC KEYNOTE talks live broadcast: September 15, 2021 
LIVE Q&A INTERACTIVE sessions for all Conference Technical sessions: September 14 – 16 – 17, 2021 
LIVE Q&A INTERACTIVE sessions for all Educational Events: September 20-21-22, 2021 
(on demand videos available from September 6 until November 30, 2021)

**Please see here for the [ESSCIRC Guide](https://drive.google.com/file/d/10dghzuv4aQBty4QfTIY9Q8hXyqsue8Le/view?usp=sharing), [ESSDERC Guide](https://drive.google.com/file/d/10dx9y2tm9cHPDa4upNUYjfXCf44aPry5/view?usp=sharing), and the [Program at a Glance](https://drive.google.com/file/d/10eU0OLTXEpiNzI5hkqv4mSr43T8cedPP/view?usp=sharing)**

To access ESSCIRC ESSDERC 2021, you must pre-register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the ESSCIRC ESSDERC conference website. [Register now](https://www.esscirc-essderc2021.org/).

Please register for the ESSCIRC conference to join the event.

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.





**Please read the paper on the following link:**

https://1worldcontent.com/ess/9017.pdf

**Abstract:**

We propose a light energy harvester that achieves harvesting efficiency of 82 – 98% at indoor light levels (70 – 1klx) and powers a millimeter-scale system even at 40lx. It improves the efficiency by connecting stacked photovoltaic cells directly to a battery without diode voltage drop and implementing essential battery protection functions in 334pA. The proposed circuit monitors open-circuit voltage of the stacked PV cells indirectly with a tiny auxiliary cell (only 0.16% area penalty) for battery discharging protection without interrupting energy harvesting. It also delays the initial battery charging by 3.9 hours to avoid battery charging during a system assembly process.

A Stacked-Photovoltaic-Cell Energy Harvester with >81% Indoor Light Harvesting Efficiency for Millimeter-Scale Energy-Autonomous Sensor Nodes

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9205844

**Abstract:**

This article focuses on one of the floating body effects in silicon-on-insulator (SOI) substrates, the out-of-equilibrium body potential. Unlike already existing studies which are exploring SOI fully-fabricated devices or SOI wafers measured with pressure probes, here the out-of-equilibrium body potential is explored in SOI substrates with deposited metal contacts. Some technological variants (such as metal choice, annealing, contact geometry) are evaluated through current and potential monitoring under quasi-static conditions. The results of our study reveal a strong dependence of the body potential on the deposited contact material and thermal post-process, while the contact geometry seems to affect less the floating body response.

Out-of-Equilibrium Body Potential Measurement on silicon-on-Insulator with Deposited Metal Contacts

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9174655

**Abstract:**

A new differential method for accurate extraction of electrical parameters of High Electron Mobility Transistor (HEMT) devices for GaN technology is proposed. This method, presented here for the first time, is used to study the mobility degradation with gate length, allowing an analysis of the contribution of the gate edge region to the total MIS-HEMT device conductance. First, an analytical model is proposed to account for the relative conduction of the edges and the main flat part of the channel HEMTs. Second, the differential method allows the split-CV mobility extraction of each part, allowing a precise analysis of the resistive contribution of each region of the HEMT devices. This study has been performed over a large range of channel lengths for two normally-off HEMT GaN wafers having different recess depths.

Analysis of MIS-HEMT Device Edge Behavior for GaN Technology Using New Differential Method

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9186308

**Abstract:**

We investigate the reliability of SiGe HBTs in 55-nm technology under mixed-mode stress. We perform electrical characterization of increased base current degradation at different collector-base voltages. The adopted modeling approach to reproduce experimental data accounts for self-heating and electric fields distribution allowing to match the deviation from the power-law behavior.

Characterization and TCAD Modeling of Mixed-Mode Stress Induced by Impact Ionization in Scaled SiGe HBTs

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9204768

**Abstract:**

Analytic doping profiles and contact resistivities are adjusted to reproduce measured transfer characteristics of state-of-the-art n-type and p-type FinFETs by Monte Carlo device simulation. The results are used to compare the performance of nanosheets (NSs) and FinFETs at advanced-node device dimensions. It is found that the ON-current normalized by the effective gate width reduces for a higher number of sheets due to a higher access resistance of the lower-lying sheets. In order to reach the same absolute current level of FinFETs with a fin height of 55 nm, more than two sheets for n-type and about four sheets for the p-type NSs with a NS width of 16 nmare needed, respectively. This technology computer-aided design (TCAD) approach can serve as input for design-technology cooptimization (DTCO) of advanced devices.

Monte Carlo Comparison of n-Type and p-Type Nanosheets with FinFETs: Effect of the Number of Sheets

**Please read the paper on the following link:**

https://1worldcontent.com/ess/9064.pdf

**Abstract:**

This paper proposes a light intensity sensing circuit for a millimeter-scale system tracking monarch butterfly migration. The system targets to be mounted on a butterfly and record light intensity during its migration.The proposed circuit contributes to light measurement of the system by consuming negative net energy, averaging light level for 1-minute sleep mode, and suppressing temperature sensitivity. A prototype system achieves energy autonomy at >300lx. A prototype circuit obtains a 3σ inaccuracy of ±1.78% and σ/µ of 2.7% from 10lx to 100klx, a temperature sensitivity of 0.035%/°C from -20 to 85°C, and a supply sensitivity of 1.08%/V from 3.6 to 4.2V.

An Energy Autonomous Light Intensity Sensor for Monarch Butterfly Migration Tracking

**Please read the paper on the following link:**

https://1worldcontent.com/ess/9101.pdf

**Abstract:**

This paper presents an energy efficient high voltage(HV) ultrasonic driver for ultrasonic haptic feedback, designed to drive Piezo electric micro machined transducers (PMUT). To achieve low power consumption while driving large capacitive load of a PMUT device, advanced charge recycling (ACR) ispresented reducing the reactive power consumption without off-chip supply generation. To facilitate ACR in a compact footprint a bidirectional high voltage switch is proposed. The ultrasonic driver is able to drive a transducer capacitance of 240pF upto 36V swing with a frequency of 250kHz, only consuming 0.2CV2f power, outperforming bulky coil-based solutions. Re-alised in a high voltage 0.18μmbulk BCD technology, the driver only occupies 0.16mm2.

A 36V Ultrasonic Driver for Haptic Feedback Using Advanced Charge Recycling Achieving 0.20CV2f Power Consumption

**Please read the paper on the following link:**

https://1worldcontent.com/ess/9126.pdf

**Abstract:**

This paper presents a compact analog front-end circuit capable of integrating with a 100µm-pitch 2D. ultrasound transducer array. To realize continuous time-gain compensation, it consists of two variable gain stages, both of which employ a novel complementary current-steering network to realize continuously-variable gain. Per channel, the AFE occupies only 0.025 mm2 and consumes 0.8 mW. It achieves an input noise density of 1.31 pA/√Hz and a gain error of ±0.4 dB, corresponding to the gain-error performance that is 2.5 times better than the prior art.

A Pitch-Matched Analog Front-End with Continuous Time-Gain Compensation for High-Density Ultrasound Transducer Arrays

**Please read the paper on the following link:**

https://1worldcontent.com/ess/9186.pdf

**Abstract:**

This work presents the first solar cell (SC)-powered biofuel cell (BFC)-input biosensing system with hybrid pulse interval modulation (PIM) and pulse density modulation (PDM) LED driving capability for stand-alone RF-less continuous glucose monitoring (CGM) contact lenses, which allows diabetes patients to obtain CGM level information without any external devices. Power supply from on-lens SCs can eliminate wireless power delivery, and LED implementation can eliminate wireless communication. The measured power of 28 nW/144 nW at PIM/PDM mode with a 0.31 V/0.39 V supply voltage can be managed by the on-lens SCs, enabling a fully stand-alone operation under office-room ambient light.

A 0.5 mm2 0.31 V/0.39 V 28 nW/144 nW 65 nm CMOS Solar Cell-Powered Biofuel Cell-Input Biosensing System with PIM/PDM LED Driving for Stand-Alone RF-Less Continuous Glucose Monitoring Contact Lens

**Please read the paper on the following link:**

https://ieeexplore.ieee.org/document/9169659

**Abstract:**

This work presents the first on-chip bio-inspired reservoir computer (RC) prototype implemented in 65nm CMOS. The RC comprises 50 time-multiplexed neurons, and each neuron embeds a strong non-linearity in a feedback loop. The RC applies a non-linear transformation to the input and projects it to high-dimensional space, thus allowing linear separation by a simple logistic regression (LR) layer implemented off-chip. We demonstrate real-time stress detection from electrocardiogram (ECG) signals using the RC. The RC achieves 93% classification accuracy, which is 6% better than state-of-the-art digital classifiers. Operating at 40kHz, the prototype consumes 27.5nJ/classification, which is 7x lower than state-of-the-art ECG processors performing similar complexity classification tasks.

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A Stacked-Photovoltaic-Cell Energy Harvester with >81% Indoor Light Harvesting Efficiency for Millimeter-Scale Energy-Autonomous Sensor Nodes

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