Sayeef Salahuddin, Professor

Closed (1) Spin Transfer torque Devices as an emerging non volatile memory technology

Applications for fall 2021 are now closed for this project.

A remarkable development in the recent years has been the demonstration that a nanoscale magnet can be switched by a spin-polarized current, without having to apply any external magnetic field. It is a fascinating phenomenon from two different perspectives. Firstly, this effect is purely mediated by quantum mechanics, but unlike most other quantum mechanical phenomena, the effect exists well beyond room temperature. Thus from scientific point of view this gives control over magnetization in a way that was not possible before. But at the same time, by virtue of not needing a magnetic field, it provides an immense potential for a high density, ultra fast and scalable non-volatile memory. Our group has been at the forefront of this development by providing a simulation platform that tries to capture and explain the essential physics and device potential of these memory devices. We shall be looking at some other exciting possibilities that one can imagine to do with this unique and fascinating spintronic device.

Qualifications: Some experience with MATLAB or C is preferred

Related website: http://eecs.berkeley.edu/~sayeef/

Closed (2) Negative Capacitance for Ultra Low Power MOSFETs

Applications for fall 2021 are now closed for this project.

It is widely believed that the rate of change in current in conventional MOSFETs cannot be decreased below 60 mV/decade. This means that to change every decade of current one must apply at least 60 mV. As a result, the power supply voltage in modern MOSFETs cannot be reduced below a certain point and it has been indicated that unless a solution is found, the scaling of MOSFETs will die a thermal death. Recently, we have shown theoretically that if a gate stack can be constructed with negative differential capacitance, the rate of change in current can be reduced below 60 mV/decade. In this project we shall try to build this gate stack. The undergraduate researchers will be helping with material growth and lithography at the nanometer scale.

Related website: http://eecs.berkeley.edu/~sayeef/

Closed (3) Algorithms and Hardware for Next Generation AI

Applications for fall 2021 are now closed for this project.

Artificial Intelligence is becoming prevalent in many applications. In this research effort we are investigating new ideas of learning and inference. In addition to develop fundamental understanding of the algorithms, we are also designing novel hardware solutions that are specifically suitable for these Learning Machines, going beyond mere implementation of the algorithms with today's computers.

The apprentice should help in both algorithm development and hardware design.

Qualifications: Some experience with Python is required. Prior background in introductory devices such as transistors and circuit design (such as EE105 and EE130) is a significant plus.

Weekly Hours: to be negotiated