This work introduces SENSOTERIC, a multi-partner project that aims at leveraging the properties of emerging Reconfigurable Field Effect Transistors (RFETs) to develop a sensor platform. RFETs will be used for a generic sensor interface and for a dedicated transducer element. In the first case, our goal is to develop an analog front-end interface that can be tuned at runtime to adapt to different environmental conditions and be used in a broad spectrum of applications. This feature shall be enabled by the polarity-control and negative differential resistance characteristics of the reconfigurable devices employed, that are co-integrable on industrial CMOS processes such as 22 nm FDSOI. In the second case, we want to exploit the intrinsic nature of these doping-free devices to yield better 1/f noise performances when compared to classic CMOS transducers. Moreover, the presence of un-gated areas on top of the channel of these devices makes them the perfect candidates to be functionalized. In this early-stage overview of the project, we will introduce the key features and the vision that make SENSOTERIC a unique contribution towards smart sensing solutions in environmental monitoring and healthcare.

Reconfigurable field-effect transistors (RFETs), allowing dynamic run-time switching between n- and p-type operation, are already considered a viable CMOS add-on technology to enable adaptive computing concepts. Beyond that, exploiting the multi-gate architecture of RFETs can also be beneficial for analog circuits but has been less explored. Here, we experimentally demonstrate an adaptive current mirror based on Ge RFETs with the ability of electrostatic compensation of device-to-device variations, enabling an ideal current mirror that can even be switched between n- and p-type operation. Furthermore, the IOUT/IIN-ratio can be adjusted electrostatically without the need for additional devices. This enhances the abilities of traditional current mirrors that are not adjustable after manufacturing without the need for complex additional circuitry.

Power attacks are effective side-channel attacks (SCAs) that exploit weaknesses in the physical implementation of a cryptographic circuit to extract its secret information like encryption key. In recent years, emerging technologies have unlocked new possibilities in designing effective SCA countermeasures with less overhead. Reconfigurable Field-Effect Transistors (RFETs) are a type of beyond-CMOS technology that can be configured at run-time to act as an NFET or PFET transistor and provide two or more independent gates. These features make RFETs potent candidates for implementing hardware security techniques like logic locking and SCA countermeasures. In this paper, we propose REDCAP, a method to add randomness to the power traces of a circuit, employing compact reconfigurable RFET-based gates to make the design resilient against power SCAs. First, we explain the construction and control of reconfigurable blocks with isofunctional configurations inside the circuit. Then, we provide an algorithm to efficiently compose the reconfigurable blocks with other circuit parts to minimize the overhead and enable designers to determine the granularity of the reconfiguration. To evaluate our approach, we performed a Correlation Power Attack (CPA) on the S-box of the Piccolo and PRESENT, two lightweight cryptographic circuits, and the results show that REDCAP can highly enhance the resilience of the circuit against power SCAs.

Side-Channel Attacks (SCAs), which are always considered a severe threat to the security of the cryptographic circuits, today can also be employed to extract IP secrets and neural network models. Hence, developing novel security solutions at different design levels is crucial. In this paper, we explore recent countermeasures at the circuit, algorithmic, and microarchitecture levels. First, we explain how Reconfigurable Field-Effect Transistor (RFET), as a beyond CMOS technology, enables us to provide both IP and data protection against SCAs at the circuit level. Second, we investigate an automated method for generating masked circuits as an algorithmic solution, and then we review machine learning-based SCA detection mechanisms at the microarchitecture level. Finally, we discuss emerging threats of SCAs from the industrial point of view.