In vivo validation of Wireless Power Transfer System for Magnetically Controlled Robotic Capsule Endoscopy

This paper presents the in vivo validation of an inductive wireless power transfer (WPT) system integrated for the first time into a magnetically controlled robotic capsule endoscopy platform. The proposed system enables continuous power delivery to the capsule without the need for onboard batteries, thus extending operational time and reducing size constraints. The WPT system operates through a resonant inductive coupling mechanism, based on a transmitting coil mounted on the end effector of a robotic arm that also houses an external permanent magnet and a localization coil for precise capsule manipulation. To ensure robust and stable power transmission in the presence of coil misalignment and rotation, a 3D receiving coil is integrated within the capsule. Additionally, a closed-loop adaptive control system, based on load-shift keying (LSK) modulation, dynamically adjusts the transmitted power to optimize efficiency while maintaining compliance with specific absorption rate (SAR) safety limits. The system has been extensively characterized in laboratory settings and validated through in vivo experiments using a porcine model, demonstrating reliable power transfer and effective robotic navigation in realistic gastrointestinal conditions: the average received power was 110 mW at a distance of 9 cm between the coils, with variable capsule rotation angles. The results confirm the feasibility of the proposed WPT approach for autonomous, battery-free robotic capsule endoscopy, paving the way for enhanced diagnostic in gastrointestinal medicine.

12-bit Delta-Sigma ADC operating at a temperature of up to 250C in Standard 0.18 $μ$m SOI CMOS

Some applications require electronic systems to operate at extremely high temperature. Extending the operating temperature range of automotive-grade CMOS processes -- through the use of dedicated design techniques -- can provide an important cost-effective advantage. We present a second-order discrete-time delta-sigma analog-to-digital converter operating at a temperature of up to 250 $^\circ$C, well beyond the 175 $^\circ$C qualification temperature of the automotive-grade CMOS process used for its fabrication (XFAB XT018). The analog-to-digital converter incorporates design techniques that are effective in mitigating the adverse effects of the high temperature, such as increased leakage currents and electromigration. We use configurations of dummy transistors for leakage compensation, clock-boosting methods to limit pass-gate cross-talk, and we optimized the circuit architecture to ensure stability and accuracy at high temperature. Comprehensive measurements demonstrate that the analog-to-digital converter achieves a signal-to-noise ratio exceeding 93 dB at 250 $^\circ$C, with an effective number of bits of 12, and a power consumption of only 44~mW. The die area of the converter is only 0.065~mm$^2$ and the area overhead of the high-temperature mitigation circuits is only 13.7%. The Schreier Figure of Merit is 140~dB at the maximum temperature of 250 $^\circ$C, proving the potential of the circuit for reliable operation in challenging applications such as gas and oil extraction and aeronautics.

Temperature-Resilient Analog Neuromorphic Chip in Single-Polysilicon CMOS Technology

In analog neuromorphic chips, designers can embed computing primitives in the intrinsic physical properties of devices and circuits, heavily reducing device count and energy consumption, and enabling high parallelism, because all devices are computing simultaneously. Neural network parameters can be stored in local analog non-volatile memories (NVMs), saving the energy required to move data between memory and logic. However, the main drawback of analog sub-threshold electronic circuits is their dramatic temperature sensitivity. In this paper, we demonstrate that a temperature compensation mechanism can be devised to solve this problem. We have designed and fabricated a chip implementing a two-layer analog neural network trained to classify low-resolution images of handwritten digits with a low-cost single-poly complementary metal-oxide-semiconductor (CMOS) process, using unconventional analog NVMs for weight storage. We demonstrate a temperature-resilient analog neuromorphic chip for image recognition operating between 10$^{\circ}$C and 60$^{\circ}$C without loss of classification accuracy, within 2\% of the corresponding software-based neural network in the whole temperature range.