In this paper, we introduce a new pneumatic temperature control technique and its application to precision thermometry. The method controls temperature by adjusting gas pressure through the unique thermohydraulic linkage of the pressure-controlled loop heat pipe (PCLHP). Due to this temperature-pressure linkage, the PCLHP-based pneumatic temperature control achieves exceptional control speed, stability, and precision. To fully understand this method, we systematically investigated the effects of various influencing parameters, such as heat load, sink temperature, and rate of pressure change, on the stability of temperature control. In addition, we successfully achieved closed-type pneumatic temperature control using a mechanically-driven gas pressure controller. We also developed a hybrid PCLHP that incorporates a heat pipe liner into the isothermal region to further improve the temperature uniformity of the pneumatically-controlled temperature field. With this technique, we significantly improved the accuracy of the fixed point of the International Temperature Scale of 1990 by using inside nucleation of the freezing temperature of tin and determining the liquidus temperature of tin. In this paper, we summarize the results of these diverse efforts in characterizing the pneumatic temperature control technique, along with theoretical analyses.

Deep reinforcement learning (RL) has attracted research interest in the manufacturing area in recent years, but real implemented applications are rarely found. This is because agents have to explore the given environments many times until they learn how to maximize the rewards for actions, which they provide to the environments. While training, random actions or exploration from agents may be disastrous in many real-world applications, and thus, people usually use computer generated simulation environments to train agents. In this paper, we present a RL experiment applied to temperature control of a chamber for ultra-precision machines. The RL agent was built in Python and PyTorch framework using a Deep Q-Network (DQN) algorithm and its action commands were sent to National Instruments (NI) hardware, which ran C codes with a sampling rate of 1 Hz. For communication between the agent and the NI data acquisition unit, a data pipeline was constructed from the subprocess module and Popen class. The agent was forced to learn temperature control while reducing the energy consumption through a reward function, which considers both temperature bounds and energy savings. Effectiveness of the RL approach to a multi-objective temperature control problem was demonstrated in this research.

In this work, recent advances in temperature control techniques and the resulting contemporary progress in precision thermometry are addressed together with a broad review of traditional temperature control methods. Particular emphases are placed on clarification of the nature of temperature control and its classification, and the relevant technical issues are addressed based on this clarification and classification. Being a thermodynamic quantity having the same dimension as energy, temperature of an object is traditionally controlled by means of the changing rate of energy (Heat) transfer; however, this approach has led to a slow, less stable, and uneven temperature field due to inherent limits caused by finite properties of materials. To overcome this problem, thermodynamic characteristics of two-phase heat transfer devices, such as heat pipes and loop heat pipes, have been extensively employed where high-speed nature of fluid flow was exploited to realize a uniform temperature field, and unique thermodynamic linkage between saturation temperature and pressure was successfully applied to attain a fast, stable, and predictable temperature control of a finite-sized isothermal space. Representative examples and applications are provided in the context of unique features of the introduced contemporary temperature control techniques, which caused significant scientific strides in the related fields.

Chemical mechanical planarization (CMP) is a wafer planarization process that uses chemical reactions initiated by slurry and mechanical actions by pad asperity. The progression of CMP causes temperature deviation on the pad surface. Increase in process temperature results in increased material removal rate (MRR). So, pad temperature distribution is closely related to With-In Wafer Non-Uniformity (WIWNU). In this study, the pad temperature distribution is modelled from the energy perspective and slurry supply location is suggested to reduce temperature deviation. An energy supplying expression was created by setting the micro area and substituting the applied pressure, relative velocity, and process time. The energy and temperature distributions were observed as quite consistent and the temperature peak matched well with highest friction heat point (HFHP). Based on the model expression, the slurry injection position was set to the center of pad, the HFHP and wafer center, and change in temperature distribution was measured. A comparative analysis was carried out employing the existing method that uses multiple nozzles rather than single nozzles and the deviation was reduced by about 18.5% when slurry was supplied to the HFHP for a single nozzle and by 24.7% when the largest flow rate was supplied for multiple nozzles.