STMicroelectronics (United States)

Abstract Background Obefazimod (Obe) is an oral, once-daily (QD), small molecule which enhances expression of microRNA-124 and has been studied in two Phase 2 induction trials and subsequent open-label maintenance studies [1-3] in patients (pts) with moderately to severely active ulcerative colitis (UC). In Phase 3 ABTECT-1 [NCT05507203] and ABTECT-2 [NCT05507216] 8-week induction trials, Obe achieved clinically meaningful improvements in clinical, endoscopic and histologic endpoints. Here, we present a pooled analysis of the efficacy and safety of Obe in the North American (NA) subgroup of pts enrolled in the ABTECT induction trials. Methods The multicenter, randomized, double-blind, placebo-controlled ABTECT trials enrolled pts with moderate-to-severe UC (MMS≥ 5, with rectal bleeding sub-score (RBS) ≥ 1 and centrally read Mayo endoscopic score ≥2) who had inadequate response, loss of response, or intolerance to at least one prior systemic therapy (corticosteroids, immunosuppressants, biologics, S1P receptor modulators and/or JAK inhibitors), with no limit on the number of prior advanced therapy inadequate responders (ATIR). Pts were randomized 2:1:1 to Obe 50 mg QD (Obe-50), Obe 25 mg QD (Obe-25) or placebo (PBO) for 8 weeks. This post-hoc analysis focuses on the subgroup of pts recruited from study defined NA centers. Efficacy endpoints included clinical remission (per MMS), clinical response, endoscopic improvement, symptomatic remission, and histo-endoscopic mucosal improvement. All p-values are nominal. Treatment emergent adverse events (TEAEs), serious TEAEs and dropout rates were evaluated. Results Among the 1272 pts randomized in the ABTECT trials, 122 were NA pts (116 pts from USA and 6 pts from Canada). Baseline demographics and disease characteristics were well balanced between treatment groups and generally comparable between NA pts and the overall study population. In the pooled analysis, a higher proportion of NA pts receiving Obe-25 or Obe-50 vs. PBO achieved clinical remission (Obe-25-PBO difference: 13.6%, p = 0.0218; Obe-50-PBO difference: 13.2%, p = 0.0235), clinical response, and symptomatic remission with nominal significance (Table). Headache was the most common TEAE (Obe-25: 29.2%; Obe-50: 24.2%; PBO: 5.6%). Among NA pts, no serious TEAEs occurred with Obe-25, and the rate with Obe-50 was similar to PBO (3.2% vs 8.3%, respectively). TEAE leading to study discontinuations were less frequent with Obe-25 (4.2%) and Obe-50 (6.5%) vs PBO (13.9%). No opportunistic infections or malignancies were reported. Conclusion In the ABTECT induction trials, clinically meaningful improvements in efficacy measures were observed, and Obe treatment demonstrated a favorable safety profile in NA pts, consistent with the overall study population.

The integration of PN diodes on bulk silicon technologies often leads to shot noise and significant parasitic effects such as latch-up which arises when a parasitic transistor is unintentionally triggered, leading to undesired current flows. To address these issues, and enable operation in higher voltage ranges, this work investigates the use of unexplored polysilicon PIN diodes implemented on an insulating area as an alternative solution to PN diodes integrated on bulk substrates. The geometric design and process optimizations of the PIN diode structure are explored to optimize its performances (forward and leakage currents, series resistance, threshold, and breakdown voltages) while ensuring compatibility with low-cost analog circuits fabricated in an 40nm technology dedicated to IoT applications. Compared to classical PN diodes, PIN diodes require a simple fabrication process and can provide a breakdown voltage twice higher. However, this improvement comes at the expense of an increased leakage current and series resistance.

Inertial sensors provide a low-power and high-fidelity pathway for state estimation and sensor fusion. Inertial measurement units now feature on-chip processors for ultra-low-power information fusion, signal processing, and neural network-based classification at the extreme edge. However, accounting for domain shifts, personalized inference requirements, and application diversity makes adopting existing learning-enabled on-device training, classification, and fusion frameworks for on-sensor processors difficult. This paper introduces a method for personalized and on-device learning for on-chip classification, inference, and information fusion applications. The proposed framework automatically segments and stores quantized gravity vector image templates and axes variance information of motion artifacts during training. During inference, templates created from the time-series windows are matched against uniform blurred templates using the universal image quality index. An adaptive rep counting module robust to varying motion primitives counts repetitions of matched motion primitives. The framework requires no human-engineered parameters and allows for the personalization and addition of new motion artifacts. Our framework recognizes human activities with 96.7% test accuracy and achieves an average rep count error of 0.44, while reducing the memory usage by 1000-2000$\times$ over existing tiny machine learning on-device learning techniques, allowing on-sensor learning and inference under 8 KB of memory.

Precision agricultural robots require high-resolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4–5.8× better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing toolbox to generate high-resolution (±5 cm) position data for fine-tuning pre-trained neural-inertial models in the field. Third, we introduce the first and largest (6.5 hours, 4.5 km, 3 phases) public neural-inertial navigation dataset for precision agricultural robots. The dataset, toolbox, and code are available at: https://github.com/nesl/agrobot.

This work describes the analysis and design of a 170W LCC converter for Lithium-ion (Li-ion) battery charger applications implemented with Gallium Nitride (GaN) technology. The solution uses an integrated System-in-Package (SiP) with two 650V enhancement mode GaN High Electron Mobility Transistors (HEMT) in Half Bridge (HB) configuration, the dedicated gate drivers and all the required protections. The LCC converter is designed to charge both 12V and 18V Li-ion batteries in CC/CV mode. The input voltage and output regulation range require a proper design of the LCC converter to shape its gain curve while maintaining the highest possible efficiency. The very high efficiency (>95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) of this design avoids the use of heatsinks and fans, allowing for a fully sealed charger with increased reliability. The efficiency and thermal performance are characterized under worst-case operating conditions in a sealed enclosure to ensure that all critical devices operating temperatures are below their rated maximum. The efficiency curves at different operating points are also provided and compared to those of a similar Silicon (Si)-based topology. When benchmarked against Si this charger shows higher efficiency in a wider regulation range, lower component temperatures and comparable system cost.

This paper presents a design solution for a PMUT RX front-end for high performance portable ultrasound medical imaging systems. The PMUT transducer is part of a ID array working in the 1-4 MHz frequency range. Adequate SNR and low power dissipation are ensured in the RX path thanks to a careful design flow focused onto specific imaging requirements, extracted considering the lumped-parameter equivalent circuit model of the transducer. Programmable gain [25dB-35dB] is implemented to comply with a wide range of input acoustic pressure while distortion parameters are designed in order to provide good imaging qualities. Transistor level design and simulations performed in a BCD-SOI 0.16-μm technology are shown as well.

The adoption of MEMS mirrors has been rapidly growing in recent years, involving different types of applications. One of the most challenging applications for Laser Beam Scanning (LBS) displays are Augmented Reality (AR) glasses, since they require high resolution images, small volume occupation and low power consumption. To comply with these requirements, MEMS scanners are moving to piezoelectric actuation, a technology that enables high actuation forces and high efficiency in a small die area. In this work, two compact MEMS mirrors with piezoelectric actuation are presented: a 27.5kHz resonant mirror (MMR40100) with 1.1mm diameter and 56deg FOV and a quasi-static mirror (MML40100) working at 60Hz with 2.45x1.44mm2 reflective area and 32deg FOV. The working voltage is &lt;40V for both mirrors, keeping the power consumption low (&lt;20mW). To enable the mirror control, diffused piezoresistive sensors in Wheatstone bridge configuration are integrated in both mirrors. In this paper it will be described the working principle of the MEMS designs, the manufacturing process, the FEM simulations and the experimental findings obtained on fabricated samples. Once coupled together, the two mirrors enable a 720p display module with 65deg diagonal FOV and a volume occupation of the entire display module of &lt;0.7cc making this solution one of the most promising for the next-generation AR glasses.

Despite the importance of separating nucleation steps from growth steps for the production of monodisperse highly luminescent In(Zn)P quantum dots (QDs), the practical implementation of this strategy is hindered by the high reactivity and fast depletion of conventional P precursors. This problem can be mitigated through the use of (i) Zn oxo clusters, which effectively regulate the kinetics of QD growth and prevent the fast depletion of conventional P precursors in the nucleation step, or (ii) seed-mediated continuous growth methods, which avoid secondary nucleation in the growth step and yield red-emitting InP QDs. Herein, we combine approaches (i) and (ii) to synthesize red-emitting In(Zn)P QDs with a high photoluminescence quantum yield (>93%) and a low emission bandwidth (full width at half maximum = 38 nm), revealing that our strategy hinders the carboxylate ketonization-induced generation of byproducts and suppresses the surface oxidation of In(Zn)P QDs during growth steps. The prepared In(Zn)P QDs are used to fabricate QD light-emitting diodes with a maximum brightness of 1164 cd m-2 and an external quantum efficiency of 3.61%. Thus, our results pave the way to the replacement of toxic Cd- and Pb-based QDs with more eco-friendly Zn- and In-based analogs for a variety of applications.

ST is the worldwide leader in the design, manufacture and sales of LBS solutions for Near-to-Eye displays, LiDAR and 3D scanning ToF applications. This presentation will be a deep dive in the key technologies at the core of LBS, enabling high performances, lightweight, small form-factor and low power consumption display projectors for all-day-wearable AR glasses in addition to the more immersive HMDs. We will introduce the ST MEMS ScanAR technology platform and product family: MEMS micromirrors, MEMS drivers, Laser Drivers and Video Processing Units, specifically designed for the today and future needs of AR.