A CMOS Temperature-to-Frequency Converter With an Inaccuracy of Less Than $\pm \hbox{0.5}\,^{\circ}{\hbox{C}}$ (3$\sigma$) From $-\hbox{40}\,^{\circ}\hbox{C}$ to 105$\,^{\circ}\hbox{C}$

Makinwa, Kofi A. A.; Snoeij, M.F.

doi:10.1109/jssc.2006.884865

Cited by 50 publications

(4 citation statements)

References 15 publications

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“…However, in Table 6 , we report sensors based on similar operating principles and on other technology in order to understand how our proposal improves the state-of-the-art. The circuit of [ 16 ] is a Silicon CMOS technology circuit based on delay-locked loops and it shows an error of between

K and

K, which is slightly lower than ours, but in a narrower temperature range, i.e.,

K. Indeed, as an example from a comparison with [ 28 ], our proposal has a greater temperature error, i.e., around

, but it can operate within a wider temperature range, even to 200 K, thanks to the higher performance of 4H-SiC technology compared to the Silicon one. Then, the proposals of [ 29 , 30 ], based on an Si 65nm-CMOS technology, use an area that is lower than ours, and in particular, [ 29 ] have

power dissipation and

temperature error, whereas [ 30 ] has, respectively,

and

compared to ours.…”

Section: Numerical Simulation Results and Process Variabilitymentioning

confidence: 84%

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K

Rinaldi,

Liguori,

May

et al. 2023

Sensors

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In this paper, we propose a temperature sensor based on a 4H-SiC CMOS oscillator circuit and that is able to operate in the temperature range between 298 K and 573 K. The circuit is developed on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology and is designed for a bias voltage of 20 V and an oscillation frequency of 90 kHz at room temperature. The possibility to relate the absolute temperature with the oscillation frequency is due to the temperature dependency of the threshold voltage and of the channel mobility of the transistors. An analytical model of the frequency-temperature dependency has been developed and is used as a starting point for the design of the circuit. Once the circuit has been designed, numerical simulations are performed with the Verilog-A BSIM4SiC model, which has been opportunely tuned on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology, and their results showed almost linear frequency-temperature characteristics with a coefficient of determination that was higher than 0.9681 for all of the bias conditions, whose maximum is 0.9992 at a VDD = 12.5 V. Moreover, we considered the effects of the fabrication process through a Monte Carlo analysis, where we varied the threshold voltage and the channel mobility with different values of the Gaussian distribution variance. For example, at VDD = 20 V, a deviation of 17.4% from the nominal characteristic is obtained for a Gaussian distribution variance of 20%. Finally, we applied the one-point calibration procedure, and temperature errors of +8.8 K and −5.8 K were observed at VDD = 15 V.

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K and

K, which is slightly lower than ours, but in a narrower temperature range, i.e.,

K. Indeed, as an example from a comparison with [ 28 ], our proposal has a greater temperature error, i.e., around

power dissipation and

temperature error, whereas [ 30 ] has, respectively,

and

compared to ours.…”

Section: Numerical Simulation Results and Process Variabilitymentioning

confidence: 84%

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K

Rinaldi,

Liguori,

May

et al. 2023

Sensors

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show abstract

“…Three examples of these techniques that are employed in the chopper amplifiers are nested chopping, spike filtering, and the use of delayed modulation or a dead-band. Figure 8a shows the schematic of the nested chopper amplifier [80][81][82][83]. As illustrated in the figure, this architecture utilizes a pair of internal and external choppers, where the frequency of the internal chopper should be chosen to be greater than 1/f noise corner frequency ( f C ).…”

Section: Systematic Techniquementioning

confidence: 99%

Multi-Channel Neural Recording Implants: A Review

Noshahr

Nabavi

Sawan

2020

Sensors

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The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain-machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.Sensors 2020, 20, 904 2 of 29 an invasive method, records proper signals. However, the disadvantage of this is not being able to record deep in the brain and they average the activity of thousands of neurons [11]. Invasive techniques have been utilized by some BMIs. This is because recording single action potentials from neurons in distributed, functionally-linked ensembles are necessary for the most accurate readout of neural activities [7]. Therefore, increasing the spatial resolution and the number of electrodes are essential for developing BMI.The implementation of a neural recording implant is multi-disciplinary, as it involves the various scientific fields such as electronics, medical, materials, electrodes, and system integration. Increasing the number of electrodes and, consequently, the number of channels (in the range of thousands), creates new challenges for neural recording in the various fields mentioned. Microelectrode technology is not appropriate for these large-scale recordings [12]. Recently, Neuralink Company has built arrays of small and flexible electrodes (3072 electrodes per array), which have enabled thousands of channel recordings [13]. In the microelectronics field, large-scale recordings create many challenges with regards to decreasing the power consumption and chip area.In the design of the neural recording implants, the two constraints, the power consumption and chip area, should be addressed. Implantable circuits should consume very low power to avoid any damage to the surrounding tissue due to gen...

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“…It is also shown that the thermal diffusivity is accurately defined in pure silicon, but is also temperature-dependent [170,246]:…”

Section: Thermal-diffusivity-based Referencesmentioning

confidence: 99%

Temperature- and Supply Voltage-Independent Time References for Wireless Sensor Networks

Smedt

Gielen

Dehaene

2015

Analog Circuits and Signal Processing

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A CMOS Temperature-to-Frequency Converter With an Inaccuracy of Less Than $\pm \hbox{0.5}\,^{\circ}{\hbox{C}}$ (3$\sigma$) From $-\hbox{40}\,^{\circ}\hbox{C}$ to 105$\,^{\circ}\hbox{C}$

Cited by 50 publications

References 15 publications

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K

Multi-Channel Neural Recording Implants: A Review

Temperature- and Supply Voltage-Independent Time References for Wireless Sensor Networks

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