A successive approximation register (SAR) analog-to-digital converter (ADC) includes a first digital-to-analog converter (DAC) coupled to receive a first input voltage to generate a first output voltage; a second DAC coupled to receive a second input voltage to generate a second output voltage; a comparator having a positive input node coupled to receive the first output voltage of the first DAC, and a negative input node coupled to receive the second output voltage of the second DAC; a SAR controller that controls switching of the first DAC and the second DAC according to a comparison output of the comparator, thereby generating an output code; a first calibration circuit coupled between the positive input node of the comparator and a ground voltage; and a second calibration circuit coupled between the negative input node of the comparator and the ground voltage.

A multiplying digital to analog converter (MDAC) includes a first resistor configured to be selectively connected to a current output node based on a first bit of a first portion of an input digital code and a second resistor configured to be selectively connected to the current output node based on a second bit of the first portion of the input digital code. A resistance of the second resistor is a resistance of the first resistor scaled by a factor. The MDAC further includes a first capacitor configured to be selectively connected to the current output node based on the first bit of the first portion and a second capacitor configured to be selectively connected to the current output node based on the second bit of the first portion. A capacitance of the second capacitor is a capacitance of the first capacitor scaled by an inverse of the factor.

A digital-to-analog converter is provided. The digital-to-analog converter comprises an input configured to receiving a first digital control code for controlling a plurality of digital-to-analog converter cells. Further, the digital-to-analog converter comprises a code converter circuit configured to converter the first digital control code to a second digital control code. Further, the digital-to-analog converter comprises a shift code generation circuit configured to generate a shift code based on a code difference between the first digital control code and a third digital control code. The digital-to-analog converter additionally comprises a bit-shifter circuit configured to bit-shift the second digital control code based on the shift code in order to obtain a modified second digital control code. The digital-to-analog converter comprises a cell activation circuit configured to selectively activate one or more of the plurality of digital-to-analog converter cells based on the modified second digital control code. Each activated digital-to-analog converter cell is configured to output a respective cell output signal. Further, the digital-to-analog converter comprises an output configured to output an analog output signal based on the cell output signals.

A digital-to-analog converter is provided. The digital-to-analog converter comprises an input configured to receiving a first digital control code for controlling a plurality of digital-to-analog converter cells. Further, the digital-to-analog converter comprises a code converter circuit configured to converter the first digital control code to a second digital control code. Further, the digital-to-analog converter comprises a shift code generation circuit configured to generate a shift code based on a code difference between the first digital control code and a third digital control code. The digital-to-analog converter additionally comprises a bit-shifter circuit configured to bit-shift the second digital control code based on the shift code in order to obtain a modified second digital control code. The digital-to-analog converter comprises a cell activation circuit configured to selectively activate one or more of the plurality of digital-to-analog converter cells based on the modified second digital control code. Each activated digital-to-analog converter cell is configured to output a respective cell output signal. Further, the digital-to-analog converter comprises an output configured to output an analog output signal based on the cell output signals.

Described herein are apparatus and methods for low speed characterization of a high-speed signal. A circuit includes a sub-sampling circuit configured to sub-sample a high-speed signal received from a device, a reconstruction loop circuit configured to reconstruct a low-speed signal from the sub-sampled high-speed signal, a low pass filter configured to filter the reconstructed low-speed signal, a discrete time low pass filter configured to mitigate skew rate requirements of the filtered low-speed signal for a digitization circuit, a continuous time low pass filter configured to smooth the skew rate mitigated low-speed signal and the digitization circuit is configured to generate a digital representation of the smoothed low-speed signal for characterization by a characterization device, and shape a noise associated with the smoothed low-speed signal outside a frequency range of interest of the smoothed low-speed signal.

Described herein are apparatus and methods for low speed characterization of a high-speed signal. A circuit includes a sub-sampling circuit configured to sub-sample a high-speed signal received from a device, a reconstruction loop circuit configured to reconstruct a low-speed signal from the sub-sampled high-speed signal, a low pass filter configured to filter the reconstructed low-speed signal, a discrete time low pass filter configured to mitigate skew rate requirements of the filtered low-speed signal for a digitization circuit, a continuous time low pass filter configured to smooth the skew rate mitigated low-speed signal and the digitization circuit is configured to generate a digital representation of the smoothed low-speed signal for characterization by a characterization device, and shape a noise associated with the smoothed low-speed signal outside a frequency range of interest of the smoothed low-speed signal.

A multi-mode processing circuit and a multi-mode controlling method thereof are provided. The multi-mode processing circuit includes, but is not limited to, a control circuit and a mixer. The control circuit is configured to receive an input signal and output one of a control signal and another control signal according to the input signal. The mixer is coupled to the control circuit and is configured to mix the control signal output by the control circuit with another input signal or mix the other control signal with the another input signal to output an output signal. Accordingly, the mixer and a buffer can be integrated into a single cell, and a fast mode switch can be achieved.

A multi-mode processing circuit and a multi-mode controlling method thereof are provided. The multi-mode processing circuit includes, but is not limited to, a control circuit and a mixer. The control circuit is configured to receive an input signal and output one of a control signal and another control signal according to the input signal. The mixer is coupled to the control circuit and is configured to mix the control signal output by the control circuit with another input signal or mix the other control signal with the another input signal to output an output signal. Accordingly, the mixer and a buffer can be integrated into a single cell, and a fast mode switch can be achieved.

A neural network scheme is described that uses unsupervised learning in oscillator neural networks. Training occurs by varying the weights in proportion to the output from a frequency detector. Inputs and initial weights are split into plurality of inputs and plurality of weights. These split inputs and weights can be analog or digital. Oscillators generate signals having frequencies that represent difference in inputs, initial weights, and adjusted factors. Frequency detectors are used to compare the oscillator frequencies with a synchronized frequency of all oscillators. The output of the frequency detectors are used to generate the adjusted factors, and in turn generate trained weights.

A neural network scheme is described that uses unsupervised learning in oscillator neural networks. Training occurs by varying the weights in proportion to the output from a frequency detector. Inputs and initial weights are split into plurality of inputs and plurality of weights. These split inputs and weights can be analog or digital. Oscillators generate signals having frequencies that represent difference in inputs, initial weights, and adjusted factors. Frequency detectors are used to compare the oscillator frequencies with a synchronized frequency of all oscillators. The output of the frequency detectors are used to generate the adjusted factors, and in turn generate trained weights.