Modern healthcare is transforming from hospital-centric to individual-centric systems. Emerging implantable and wearable medical (IWM) devices are integral parts of enabling affordable and accessible healthcare. Early disease diagnosis and preventive measures are possible by continuously monitoring clinically significant physiological parameters. However, most IWM devices are battery-operated, requiring replacement, which interrupts the proper functioning of these devices. For the continuous operation of medical devices for an extended period of time, supplying uninterrupted energy is crucial. A sustainable and health-compatible energy supply will ensure the high-performance real-time functioning of IWM devices and prolong their lifetime. Therefore, harvesting energy from the human body and ambient environment is necessary for enduring precision healthcare and maximizing user comfort. Energy harvesters convert energy from various sources into an equivalent electrical form. This paper presents a state-of-the-art comprehensive review of energy harvesting techniques focusing on medical applications. Various energy harvesting approaches, working principles, and the current state are discussed. In addition, the advantages and limitations of different methods are analyzed and existing challenges and prospects for improvement are outlined. This paper will help with understanding the energy harvesting technologies for the development of high-efficiency, reliable, robust, and battery-free portable medical devices.

Ferroelectrics offer a promising material platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET operation entails a fundamental trade-off between the read and the program operations. To overcome this trade-off, we propose in this work, a novel device concept, Mott-FeFET, that aims to replace the Silicon channel of the FeFET with VO₂- a material that exhibits an electrically driven insulator–metal phase transition. The Mott-FeFET design, which demonstrates a (ferroelectric) polarization-dependent threshold voltage, enables the read current distinguishability (i.e., the ratio of current sensed when the Mott-FeFET is in state 1 and 0, respectively) to be independent of the program voltage. This enables the device to be programmed at low voltages without affecting the ability to sense/read the state of the device. Our work provides a pathway to realize low-voltage and energy-efficient non-volatile memory solutions.

This paper presents the design of an optimized hardware-based neural network (NN) called a Shift-Accumulate Binarized Neural Network (SABiNN). SABiNN is employed in detecting respiratory-related diseases such as sleep apnea (SA) among adults. Initially, a 3-hidden layer-based NN model was trained, validated, and tested with open-source apnea PSG datasets collected from the PhysioNET databank. Single lead ECG and pulse oximeter data were collected, pre-processed, and digitized for network training. The NN was later transformed into SABiNN, demonstrating model accuracy of 81.5% (CI: ± 3.5) with an energy efficiency of 5mJ on reprogrammable hardware. The precision rate of the model was further increased by re-designing the XNOR gate of the multiply-accumulate (MAC) operation with NAND gate-based XNOR. This re-design process significantly improved the overall model’s classification and precision. Further expansion of SABiNN was carried out to achieve a higher accuracy rate (over 88%) which was designed on the CMOS platform using a 130 nm open-source process design kit (PDK) developed by Google and Skywater. The proposed model on the CMOS platform utilized a chip area of 0.16 mm ² and showcased an energy efficiency of 1pJ. A total of ~11k CMOS cells with two 16-bit input and one 1-bit output pins were utilized to realize the SABiNN on CMOS. The success of this design technique can be leveraged in developing a fully system-on-a-chip (SoC) integrated wearable system for sleep apnea detection.