Narrow width, bulk charge, and DIBL effects.
ICIRCUIT REFERENCE SERIES
Ids, conductances and their derivatives throughout allĬompared with the previous BSIM models, the improved model continuityĮnhances the convergence property of the circuit simulators.įurthermore, the model accuracy has also been enhanced by including theĭependencies of geometry and bias of parasitic series resistances, With a single I-V expression, and guarantees the continuities of Inversion as well as from the linear to the saturation operating regions The model describes current characteristics from subthreshold to strong Including the major physical effects in state-of-the art MOS devices, Model) I-V model in BSIM3v3 is presented for circuit simulation. The devices biased in saturation operate in strong inversion,įor which accurate device models are usually available, simplifying theĭesign process, especially in digital CMOS technologiesĪ new physical and continuous BSIM (Berkeley Short-Channel IGFET It uses only MOS transistors biased in saturation orĬutoff. Presents a circuit solution to the above problem: a bandgap reference One way to overcome this problem is to use an extra mask to selectivelyīlock the silicide, but this mask also increases the cost. Susceptibility of the reference operation to substrate noise coupling. Resistors is increased, increasing not only the cost, but also the
As a result, the length and area of the required Often used to reduce the sheet resistance of the polysilicon andĭiffusion layers. Resistors is increased in standard digital processes because silicide is Values are available in analog CMOS processes, the area of such Although inexpensive resistors of suitable Relative weighting of the voltages added is usually adjusted by trimming Produce an output that is insensitive to changes in temperature. With a voltage that is proportional to absolute temperature (PTAT) to
The above results prove that that thermal stability and the simulation reliability can be co-designed with the minimal area cost.īandgap references add the forward bias voltage across a pn diode According to the thermal factor defined in co-simulation, the working temperature decreases 10 ☌, while the area only increases 27%. The errors between simulation and measurement are reduced from 6% to 1%. The experimental results show that the variations in power added efficiency and output power is stabilized due to the 3.5% error ZTAT current. To verify the proposed method, a sub-6G PA is realized in the GaAs HBT process. Specifically, an adjustable bias circuit is applied on the PA for temperature compensation using a zero-to-absolute-temperature (ZTAT) current. To reduce the parameter errors caused by the variations of thermal resistances, a co-simulation method for the multiple heat sources of fully-integrated PA is proposed. In this paper, the thermal stability of GaAs heterojunction bipolar transistor (HBT) power amplifier (PA) in a sub-6G band has been improved by a multi-source co-simulation method. The following describes the details of our current reference circuit. The current reference circuit we propose will solve these problems and can be used for low- power subthreshold MOS LSIs. Therefore, they are unsuitable for our purpose. The latter shows a zero temperature coefficient but consists of a complex circuit with many MOSFETs. The former current reference, however, shows a positive temperature coefficient of output current. Current references for subthreshold CMOS circuits have been proposed by Oguey and Aebischer (1), and Hirose and others (2, 3). We propose such a reference current circuit that is insensitive to temperature and power supply voltage. For this purpose, we need to develop a circuit that provides a small reference current of less than tens of nanoamperes for the subthreshold CMOS circuits.
To achieve such ultra-low power operation, CMOS circuits in sensor LSIs have to be operated in the subthreshold region of MOSFETs. These LSIs have to operate with a low power, tens of microwatts or less, because they will probably be placed in non-ideal environments where energy for operation cannot be obtained sufficiently. Intelligent network systems for the future will require a great number of smart sensor LSIs that measure various physical data in surroundings.
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