A Design of 10-Bit Asynchronous SAR ADC with an On-Chip Bandgap Reference Voltage Generator.

A proposed prototype of a 10-bit 1 MS/s single-ended asynchronous Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) with an on-chip bandgap reference voltage generator is fabricated with 130 nm technology. To optimize the power consumption, static, and dynamic performance, several techniques have been proposed. A dual-path bootstrap switch was proposed to increase the linearity sampling. The Voltage Common Mode (VCM)-based Capacitive Digital-to-Analog Converter (CDAC) switching technique was implemented for the CDAC part to alleviate the switching energy problem of the capacitive DAC. The proposed architecture of the two-stage dynamic latch comparator provides high speed and low power consumption. Moreover, to achieve faster bit conversion with an efficient time sequence, asynchronous SAR logic with an internally generated clock is implemented, which avoids the requirement of a high-frequency external clock, as all conversions are carried out in a single clock cycle. The proposed error amplifier-based bandgap reference voltage generator provides a stable reference voltage to the ADC for practical implementation. The measurement results of the proposed SAR ADC, including an on-chip bandgap reference voltage generator, show an Effective Number of Bits (ENOB) of 9.49 bits and Signal-to-Noise and Distortion Ratio (SNDR) of 58.88 dB with 1.2 V of power supply while operating with a sampling rate of 1 MS/s.

A Configurable and Fully Synthesizable RTL-Based Convolutional Neural Network for Biosensor Applications.

This paper presents a register-transistor level (RTL) based convolutional neural network (CNN) for biosensor applications. Biosensor-based diseases detection by DNA identification using biosensors is currently needed. We proposed a synthesizable RTL-based CNN architecture for this purpose. The adopted technique of parallel computation of multiplication and accumulation (MAC) approach optimizes the hardware overhead by significantly reducing the arithmetic calculation and achieves instant results. While multiplier bank sharing throughout the convolutional operation with fully connected operation significantly reduces the implementation area. The CNN model is trained in MATLAB® on MNIST® handwritten dataset. For validation, the image pixel array from MNIST® handwritten dataset is applied on proposed RTL-based CNN architecture for biosensor applications in ModelSim®. The consistency is checked with multiple test samples and 92% accuracy is achieved. The proposed idea is implemented in 28 nm CMOS technology. It occupies 9.986 mm2 of the total area. The power requirement is 2.93 W from 1.8 V supply. The total time taken is 8.6538 ms.

A Highly Efficient RF-DC Converter for Energy Harvesting Applications Using a Threshold Voltage Cancellation Scheme.

In this paper, a self-threshold voltage (Vth) compensated Radio Frequency to Direct Current (RF-DC) converter operating at 900 MHz and 2.4 GHz is proposed for RF energy harvesting applications. The threshold voltage of the rectifying devices is compensated by the bias voltage generated by the auxiliary transistors and output DC voltage. The auxiliary transistors compensate the threshold voltage (Vth) of the PMOS rectifying device while the threshold voltage (Vth) of the NMOS rectifying device is compensated by the output DC voltage. The proposed RF-DC converter was implemented in 180 nm Complementary Metal-Oxide Semiconductor (CMOS) technology. The experimental results show that the proposed design achieves better performance at both 900 MHz and 2.4 GHz frequencies in terms of PCE, output voltage, sensitivity, and effective area. The peak power conversion efficiency (PCE) of 38.5% at -12 dBm across a 1 MΩ load for 900 MHz frequency was achieved. Similarly, for 2.4 GHz frequency, the proposed circuit achieves a peak PCE of 26.5% at -6 dBm across a 1 MΩ load. The proposed RF-DC converter circuit shows a sensitivity of -20 dBm across a 1 MΩ load and produces a 1 V output DC voltage.

A 0.617-2.7 GHz Highly Linear High-Power Dual Port 15 Throws Antenna Switch Module (DP15T-ASM) with Branched-Antenna Technique and Termination Mode.

This paper presents a Dual-Port-15-Throw (DP15T) antenna switch module (ASM) Radio Frequency (RF) switch implemented by a branched antenna technique which has a high linearity for wireless communications and various frequency bands, including a low- frequency band of 617-960 MHz, a mid-frequency band of 1.4-2.2 GHz, and a high-frequency band of 2.3-2.7 GHz. To obtain an acceptable Insertion Loss (IL) and provide a consistent input for each throw, a branched antenna technique is proposed that distributes a unified magnetic field at the inputs of the throws. The other role of the proposed antenna is to increase the inductance effects for the closer ports to the antenna pad in order to decrease IL at higher frequencies. The module is enhanced by two termination modes for each antenna path to terminate the antenna when the switch is not operating. The module is fabricated in the silicon-on-insulator CMOS process. The measurement results show a maximum IMD2 and IMD3 of -100 dBm, while for the second and third harmonics the maximum value is -89 dBc. The module operates with a maximum power handling of 35 dBm. Experimental results show a maximum IL of 0.34 and 0.92 dB and a minimum isolation of 49 dB and 35.5 dB at 0.617 GHz and 2.7 GHz frequencies, respectively. The module is implemented in a compact way to occupy an area of 0.74 mm2. The termination modes show a second harmonic of 75 dBc, which is desirable.

A Low-Power Analog Processor-in-Memory-Based Convolutional Neural Network for Biosensor Applications.

This paper presents an on-chip implementation of an analog processor-in-memory (PIM)-based convolutional neural network (CNN) in a biosensor. The operator was designed with low power to implement CNN as an on-chip device on the biosensor, which consists of plates of 32 × 32 material. In this paper, 10T SRAM-based analog PIM, which performs multiple and average (MAV) operations with multiplication and accumulation (MAC), is used as a filter to implement CNN at low power. PIM proceeds with MAV operations, with feature extraction as a filter, using an analog method. To prepare the input feature, an input matrix is formed by scanning a 32 × 32 biosensor based on a digital controller operating at 32 MHz frequency. Memory reuse techniques were applied to the analog SRAM filter, which is the core of low power implementation, and in order to accurately grasp the MAC operational efficiency and classification, we modeled and trained numerous input features based on biosignal data, confirming the classification. When the learned weight data was input, 19 mW of power was consumed during analog-based MAC operation. The implementation showed an energy efficiency of 5.38 TOPS/W and was differentiated through the implementation of 8 bits of high resolution in the 180 nm CMOS process.

A Low-Power 12-Bit 20 MS/s Asynchronously Controlled SAR ADC for WAVE ITS Sensor Based Applications.

A low power 12-bit, 20 MS/s asynchronously controlled successive approximation register (SAR) analog-to-digital converter (ADC) to be used in wireless access for vehicular environment (WAVE) intelligent transportation system (ITS) sensor based application is presented in this paper. To optimize the architecture with respect to power consumption and performance, several techniques are proposed. A switching method which employs the common mode charge recovery (CMCR) switching process is presented for capacitive digital-to-analog converter (CDAC) part to lower the switching energy. The switching technique proposed in our work consumes 56.3% less energy in comparison with conventional CMCR switching method. For high speed operation with low power consumption and to overcome the kick back issue in the comparator part, a mutated dynamic-latch comparator with cascode is implemented. In addition, to optimize the flexibility relating to the performance of logic part, an asynchronous topology is employed. The structure is fabricated in 65 nm CMOS process technology with an active area of 0.14 mm2. With a sampling frequency of 20 MS/s, the proposed architecture attains signal-to-noise distortion ratio (SNDR) of 65.44 dB at Nyquist frequency while consuming only 472.2 µW with 1 V power supply.

A Design of Adaptive Control and Communication Protocol for SWIPT System in 180 nm CMOS Process for Sensor Applications.

This paper presents an adaptive control and communication protocol (ACCP) for the ultra-low power simultaneous wireless information and power transfer (SWIPT) system for sensor applications. The SWIPT system-related operations depend on harvested radio frequency (RF) energy from the ambient environment. The necessary power for SWIPT system operation is not always available and it depends on the available RF energy in the ambient environment, transmitted RF power from the SWIPT transmitter, and the distance from the transmitter and channel conditions. Thus, an efficient control and communication protocol is required which can control the SWIPT system for sensor applications which mainly consists of a transmitter and a receiver. Multiple data frame structures are used to optimize the exchange of bits for the communication and control of the SWIPT system. Both SWIPT transmitter and receiver are capable of using multiple modulation schemes which can be switched depending on the channel condition and the available RF energy in the ambient environment. This provides support for automatic switching between the time switching scheme and power splitting scheme for the distribution of received RF power in the SWIPT receiver. It also adjusts the digital clock frequency at the SWIPT receiver according to the harvested power level to optimize the power consumption. The SWIPT receiver controller with ACCP is implemented in 180 nm CMOS technology. The RF frequency of the SWIPT operation is 5.8 GHz. Digital clock frequency at the SWIPT receiver can be adjusted between 32 kHz and 2 MHz which provides data rates from 8 to 500 kbps, respectively. The power consumption and area utilization are 12.3 µW and 0.81 mm².

A Fully Integrated Bluetooth Low-Energy Transceiver with Integrated Single Pole Double Throw and Power Management Unit for IoT Sensors.

This paper presents a low power Gaussian Frequency-Shift Keying (GFSK) transceiver (TRX) with high efficiency power management unit and integrated Single-Pole Double-Throw switch for Bluetooth low energy application. Receiver (RX) is implemented with the RF front-end with an inductor-less low-noise transconductance amplifier and 25% duty-cycle current-driven passive mixers, and low-IF baseband analog with a complex Band Pass Filter(BPF). A transmitter (TX) employs an analog phase-locked loop (PLL) with one-point GFSK modulation and class-D digital Power Amplifier (PA) to reduce current consumption. In the analog PLL, low power Voltage Controlled Oscillator (VCO) is designed and the automatic bandwidth calibration is proposed to optimize bandwidth, settling time, and phase noise by adjusting the charge pump current, VCO gain, and resistor and capacitor values of the loop filter. The Analog Digital Converter (ADC) adopts straightforward architecture to reduce current consumption. The DC-DC buck converter operates by automatically selecting an optimum mode among triple modes, Pulse Width Modulation (PWM), Pulse Frequency Modulation (PFM), and retention, depending on load current. The TRX is implemented using 1P6M 55-nm Complementary Metal-Oxide-Semiconductor (CMOS) technology and the die area is 1.79 mm2. TRX consumes 5 mW on RX and 6 mW on the TX when PA is 0-dBm. Measured sensitivity of RX is -95 dBm at 2.44 GHz. Efficiency of the DC-DC buck converter is over 89% when the load current is higher than 2.5 mA in the PWM mode. Quiescent current consumption is 400 nA from a supply voltage of 3 V in the retention mode.

Design of a 900 MHz Dual-Mode SWIPT for Low-Power IoT Devices.

This paper presents a duty cycle-based, dual-mode simultaneous wireless information and power transceiver (SWIPT) for Internet of Things (IoT) devices in which a sensor node monitors the received power and adaptively controls the single-tone or multitone communication mode. An adaptive power-splitting (PS) ratio control scheme distributes the received radio frequency (RF) energy between the energy harvesting (EH) path and the information decoding (ID) path. The proposed SWIPT enables the self-powering of an ID transceiver above 20 dBm input power, leading to a battery-free network. The optimized PS ratio of 0.44 enables it to provide sufficient harvested energy for self-powering and energy-neutral operation of the ID transceiver. The ID transceiver can demodulate the amplitude-shift keying (ASK) and the binary phase-shift keying (BPSK) signals. Moreover, for low-input power level, a peak-to-average power ratio (PAPR) scheme based on multitone is also proposed for demodulation of the information-carrying RF signals. Due to the limited power, information is transmitted in uplink by backscatter modulation instead of RF signaling. To validate our proposed SWIPT architecture, a SWIPT printed circuit board (PCB) was designed with a multitone SWIPT board at 900 MHz. The demodulation of multitone by PAPR was verified separately on the PCB. Results showed the measured sensitivity of the SWIPT to be -7 dBm, and the measured peak power efficiency of the RF energy harvester was 69% at 20 dBm input power level. The power consumption of the injection-locked oscillator (ILO)-based phase detection path was 13.6 mW, and it could be supplied from the EH path when the input power level was high. The ID path could demodulate 4-ASK- and BPSK-modulated signals at the same time, thus receiving 3 bits from the demodulation process. Maximum data rate of 4 Mbps was achieved in the measurement.