Perspectives on Design Considerations Inspired by Security and Quantum Technology in Cyberphysical Systems for Process Engineering

Advances in computer science have been a driving force for change in process systems engineering for decades. Faster computers, expanded computing resources, simulation software, and improved optimization algorithms have all changed chemical engineers’ abilities to predict, control, and optimize process systems. Two newer areas relevant to computer science that are impacting process systems engineering are cybersecurity and quantum computing. This work reviews some of our group’s recent work in control-theoretic approaches to control system cybersecurity and touches upon the use of quantum computers, with perspectives on the relationships between process design and control when cybersecurity and quantum technologies are of interest

Computational fluid dynamics modeling of a wafer etch temperature control system

Next-generation etching processes for semiconductor manufacturing exploit the potential of a variety of operating conditions, including cryogenic conditions at which high etch rates of silicon and very low etch rates of the photoresist are achieved. Thus, tight control of wafer temperature must be maintained. However, large and fast changes in the operating conditions make the wafer temperature control very challenging to be performed using typical etch cooling systems. The selection and evaluation of control tunings, material, and operating costs must be considered for next-generation etching processes under different operating strategies. These evaluations can be performed using digital twin environments (which we define in this paper to be a model that captures the major characteristics expected of a typical industrial process). Motivated by this, this project discusses the development of a computational fluid dynamics (CFD) model of a wafer temperature control (WTC) system that we will refer to as a “digital twin” due to its ability to capture major characteristics of typical wafer temperature control processes. The steps to develop the digital twin using the fluid simulation software ANSYS Fluent are described. Mesh and time independence tests are performed with a subsequent benchmark of the proposed ANSYS model with etch cooling system responses that meet expectations of a typical industrial cooling system. In addition, to quickly test different operating strategies, we propose a reduced-order model in Python based on ANSYS simulation data that is much faster to simulate than the ANSYS model itself. The reduced-order model captures the major features of the WTC system demonstrated in the CFD simulation results. Once the operating strategy is selected, this could be implemented in the digital twin using ANSYS to view flow and temperature profiles in depth

Challenges and Opportunities for Next-Generation Manufacturing in Space

With commercial space travel now a reality, the idea that people might spend time on other planets in the future seems to have greater potential. To make this possible, however, there needs to be flexible means for manufacturing in space to enable tooling or resources to be created when needed to handle unexpected situations. Next-generation manufacturing paradigms offer significant potential for the kind of flexibility that might be needed; however, they can result in increases in computation time compared to traditional control methods that could make many of the computing resources already available on earth attractive for use. Furthermore, resilience is a significant focus of next-generation manufacturing strategies, and one way to enable resilience for space manufacturing would be to have backup controllers available on earth. These types of considerations raise questions about remote control and monitoring, as well as privacy of the data involved in such practices, that must be considered. This work provides a perspective on several topics tied to remote control and monitoring for manufacturing in space

Development of directed randomization for discussing a minimal security architecture

Strategies for mitigating the impacts of cyberattacks on control systems using a control-oriented perspective have become of greater interest in recent years. Our group has contributed to this trend by developing several methods for detecting cyberattacks on process sensors, actuators, or both sensors and actuators simultaneously using an advanced optimization-based control strategy known as Lyapunov-based economic model predictive control (LEMPC). However, each technique comes with benefits and limitations, both with respect to one another and with respect to traditional information technology and computer science-type approaches to cybersecurity. An important question to ask, therefore, is what the goal should be of the development of new control-based techniques for handling cyberattacks on control systems, and how we will be able to benchmark these as “successful” compared to other techniques to drive development or signal when the research in this direction has reached maturity. In this paper, we propose that the goal of research in control system cybersecurity for next-generation manufacturing should be the development of a security architecture that provides flexibility and safety with lowest cost, and seek to clarify this concept by re-analyzing some of the security techniques from our prior work in such a context. We also show how new methods can be developed and analyzed within this “minimum security architecture” context by proposing a technique which we term “directed randomization” that may require less sensors to be secured in a system than some of our prior methods, potentially adding flexibility to the system while still maintaining security. Directed randomization seeks to utilize the existence of two possible stabilizing inputs at every sampling time to attempt to create a challenge for an attacker for setting up an arbitrary sensor attack policy without being detected within a finite number of sampling periods. We discuss benefits and limitations of this technique with respect to our prior cybersecurity strategies and also with respect to extended versions of these prior concepts, such as image-based control and distributed control, to provide further insights into the minimum security concep

Control Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives

Cybersecurity and dynamic operation in practice: Equipment impacts and safety guarantees

Though dynamic operation of chemical processes has been extensively explored theoretically in contexts such as economic model predictive control or even considering the potential for cyberattacks on control systems creating non-standard operating policies, important practical questions remain regarding dynamic operation. In this work, we look at two of these with particular relevance to process safety: (1) evaluating dynamic operating policies with respect to process equipment fidelity and (2) evaluating procedures for determining the parameters of an advanced control law that can promote both dynamic operation as well as safety if appropriately designed. Regarding the first topic, we utilize computational fluid dynamics and finite element analysis simulations to analyze how cyberattacks on control systems could impact a metric for stress in equipment (maximum Von Mises stress) over time. Subsequently, we develop reduced-order models showing how both a process variable and maximum Von Mises stress vary over time in response to temperature variations at the boundary of the equipment, to use in evaluating how advanced control frameworks might impact and consider the stress. We close by investigating options for obtaining parameters of an economic model predictive control design that would need to meet a variety of theoretical requirements for safety guarantees to hold. This provides insights on practical safety aspects of control theory, and also indicates relationships between control and design from a safety perspective that highlight further relationships between design and control under dynamic operation to deepen perspectives from the computational fluid dynamics and finite element analysis discussions

Computational fluid dynamics modeling of a wafer etch temperature control system

Next-generation etching processes for semiconductor manufacturing exploit the potential of a variety of operating conditions, including cryogenic conditions at which high etch rates of silicon and very low etch rates of the photoresist are achieved. Thus, tight control of wafer temperature must be maintained. However, large and fast changes in the operating conditions make the wafer temperature control very challenging to be performed using typical etch cooling systems. The selection and evaluation of control tunings, material, and operating costs must be considered for next-generation etching processes under different operating strategies. These evaluations can be performed using digital twin environments (which we define in this paper to be a model that captures the major characteristics expected of a typical industrial process). Motivated by this, this project discusses the development of a computational fluid dynamics (CFD) model of a wafer temperature control (WTC) system that we will refer to as a “digital twin” due to its ability to capture major characteristics of typical wafer temperature control processes. The steps to develop the digital twin using the fluid simulation software ANSYS Fluent are described. Mesh and time independence tests are performed with a subsequent benchmark of the proposed ANSYS model with etch cooling system responses that meet expectations of a typical industrial cooling system. In addition, to quickly test different operating strategies, we propose a reduced-order model in Python based on ANSYS simulation data that is much faster to simulate than the ANSYS model itself. The reduced-order model captures the major features of the WTC system demonstrated in the CFD simulation results. Once the operating strategy is selected, this could be implemented in the digital twin using ANSYS to view flow and temperature profiles in depth