What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Amplifier
- Voltage
Franco Maloberti mainly focuses on Electronic engineering, CMOS, Electrical engineering, Operational amplifier and Delta-sigma modulation.
His biological study focuses on Bandwidth.
His CMOS research includes elements of Chip, Successive approximation ADC, Linearity, Very-large-scale integration and Transistor.
His work deals with themes such as Dynamic range and Low-power electronics, which intersect with Electrical engineering.
The study incorporates disciplines such as Attenuation and Integrator in addition to Operational amplifier.
His Delta-sigma modulation study incorporates themes from Network topology, Band-pass filter and Intermediate frequency.
His most cited work include:
- A 9.4-ENOB 1V 3.8μW 100kS/s SAR ADC with Time-Domain Comparator (133 citations)
- MODELING SIGMA-DELTA MODULATOR NON-IDEALITIES IN SIMULINK ® (90 citations)
- A 4-Output Single-Inductor DC-DC Buck Converter with Self-Boosted Switch Drivers and 1.2A Total Output Current (71 citations)
What are the main themes of his work throughout his whole career to date?
Franco Maloberti mainly investigates Electronic engineering, Electrical engineering, CMOS, Delta-sigma modulation and Voltage.
His work in Electronic engineering tackles topics such as Modulation which are related to areas like Control theory.
His research on Electrical engineering frequently links to adjacent areas such as Low-power electronics.
His biological study spans a wide range of topics, including Successive approximation ADC, Linearity, Switched capacitor, Bandwidth and Transistor.
His studies in Delta-sigma modulation integrate themes in fields like Integrator, Noise shaping, Quantization and Oversampling.
His research in Voltage is mostly concerned with Inductor.
He most often published in these fields:
- Electronic engineering (73.04%)
- Electrical engineering (44.78%)
- CMOS (36.74%)
What were the highlights of his more recent work (between 2012-2020)?
- Electronic engineering (73.04%)
- Electrical engineering (44.78%)
- CMOS (36.74%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Electronic engineering, Electrical engineering, CMOS, Voltage and Capacitor.
Franco Maloberti has researched Electronic engineering in several fields, including Transistor, Converters, Operational amplifier and Successive approximation ADC.
The various areas that Franco Maloberti examines in his CMOS study include Comparator, Figure of merit, Chip, Offset and Bandwidth.
His Bandwidth research is multidisciplinary, incorporating elements of Power consumption and Digital biquad filter.
Franco Maloberti combines subjects such as Hall effect sensor and Sensitivity with his study of Voltage.
His Capacitor research is multidisciplinary, incorporating perspectives in Capacitive sensing, Shaping and Integrated circuit.
Between 2012 and 2020, his most popular works were:
- Split-SAR ADCs: Improved Linearity With Power and Speed Optimization (46 citations)
- A System for Wireless Power Transfer of Micro-Systems In-Vivo Implantable in Freely Moving Animals (39 citations)
- CMOS Vertical Hall Magnetic Sensors on Flexible Substrate (36 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Amplifier
- Voltage
His primary areas of investigation include Electrical engineering, Electronic engineering, CMOS, Hall effect sensor and Voltage.
Franco Maloberti specializes in Electronic engineering, namely Spurious-free dynamic range.
Franco Maloberti has researched CMOS in several fields, including Control theory, Inductor, Figure of merit, Chip and Electronics.
His Hall effect sensor research is multidisciplinary, incorporating elements of Acoustics, Current mode, Nuclear magnetic resonance and Sensitivity.
In the subject of general Voltage, his work in Biasing is often linked to Ion, thereby combining diverse domains of study.
His research in Operational amplifier intersects with topics in Delta-sigma modulation and Chopper.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
