Design-Technology Co-Optimization (DTCO) can be significantly accelerated by employing Neural Compact Models (NCMs). However, the effective deployment of NCMs requires a substantial amount of training data for accurate device modeling. This paper introduces an Active Learning (AL) framework designed to enhance the efficiency of both device modeling and process optimization, particularly addressing the challenges of time-intensive Technology Computer-Aided Design (TCAD) simulations. The framework employs a ranking algorithm that assesses metrics such as the expected variance from the neural tangent kernel (NTK), TCAD simulation time, and the complexity of I-V curves. This strategy considerably reduces the number of required simulations while maintaining high accuracy. Demonstrating the effectiveness of our AL framework, we achieved a 28.5% improvement in MSE within a 30-minute time budget for device modeling, and an 86.7% reduction in the data points required for process optimization of a 51-stage ring oscillator (RO). These results offer a streamlined, adaptable solution for rapid device modeling and process optimization in various DTCO applications.

Many industries, including manufacturing, are adopting data analytics (DA) in making decisions to improve quality, cost, and on-time delivery. In recent years, more research and development efforts have applied DA to additive manufacturing (AM) decision-making problems such as part design and process planning. Though there are many AM decision-making problems, not all benefit greatly from DA. This may be due to insufficient AM data, unreliable data quality, or the fact that DA is not cost effective when it is applied to some AM problems. This paper proposes a framework to investigate DA opportunities in a manufacturing operation, specifically AM. The proposed framework identifies and prioritizes AM potential opportunities where DA can make impact. The proposed framework is presented in a five-tier architecture, including value, decision-making, data analytics, data, and data source tiers. A case study is developed to illustrate how the proposed framework identifies DA opportunities in AM.

The paper describes process and resource modeling capabilities of PRISM (process and resource model-based intelligent simulator). The former allows users to model nonlinear process plans that contain all possible routings through the processes in the shop, decision-making rules associated with those routings, and precedence relationships among the processes in those routings. The latter allows users to define all of the properties of shop floor resources and decision rules associated with those resources.

Flexibility has become an important characteristic of today's manufacturing industry. Unfortunately, today's standard of using a fixed (linear) process plan is not flexible enough to adapt to different manufacturing system conditions aiming at increasing the system's efficiency. One means to overcome such a limitation is to prepare pre-planned alternatives in the process plan. If this can be easily done and implemented, the most appropriate plan can be selected in real time from all possible alternatives according to the conditions of the shop floor. The objective of the paper is to demonstrate this concept in detail and to discuss the influence of process plan alternatives in a dynamic shop floor environment at equipment level. In order to verify whether the presence of alternatives in process plans increases the efficiency of the manufacturing system, a simulation will be carried out using some example parts currently being manufactured in Penn State's Factory for Advanced Manufacturing Education (FAME).

Flexible manufacturing systems are capable of producing a broad variety of products and changing their characteristics quickly and frequently. This flexibility provides for more efficient use of resources, but makes control of these systems more difficult. Control problems previously unstudied now require practical resolution, like system deadlock. A system deadlock is a situation that arises due to resource sharing in manufacturing systems, when the flow of parts is permanently inhibited and/or operations on parts cannot be performed. This problem has been ignored by most scheduling and control studies, which usually assume infinite machine queue capacity and unlimited tooling resources. FMS's, however, have little or no queue capacity and limited tooling resources. In this paper, graph-theoretic deadlock detection and resolution procedures are presented which are suitable for real-time control of manufacturing systems. These procedures determine whether part movement in the system causes system deadlock or not. To this end, a system status graph representing part routings is virtually updated for every part movement before parts move physically to the next destination. Two types of system deadlocks, part flow deadlock and impending part flow deadlock, are detected using the updated system status graph. If a deadlock detection and recovery method is used to recover from a deadlock using a storage buffer, only part flow deadlocks need to be detected. On the other hand, if no buffer is available, both types of existing as well as impending system deadlocks need to be detected to avoid a deadlock situation.>