The continuous demand for high-speed, energy-efficient computing has intensified the exploration of in- memory analog computing (IMAC) architectures, which aim to minimize the latency and power overhead associated with conventional von Neumann architectures. This research investigates the integration of phase- change materials (PCMs) with semiconductor circuits to realize programmable, non-volatile in-memory analog computing platforms. PCMs exhibit reversible resistance switching between amorphous and crystalline states, enabling precise analog weight storage directly within computational elements. By embedding PCMs in CMOS- compatible circuits, the proposed architecture facilitates parallelized matrix-vector multiplication, critical for accelerating machine learning and signal processing tasks. Comprehensive device-level simulations demonstrate that PCM-integrated circuits can achieve low write energy (~pJ per operation) and high endurance (>10^9 cycles) while maintaining computational accuracy within 2% of ideal analog behavior. Additionally, circuit- level analyses reveal enhanced scalability and reduced area footprint compared to conventional SRAM-based analog computing units. In order to increase long-term dependability and computational efficiency, the study also looks at the thermal stability, switching dynamics, and data retention properties of PCM devices under various operating situations. The study provides a framework for optimizing PCM device parameters, circuit layouts, and analog computation strategies to maximize speed, energy efficiency, and reliability. These findings underscore the potential of PCM-enabled in-memory analog computing as a viable pathway for next-generation edge AI and neuromorphic systems.

Keywords - Phase-Change Material, In-Memory Analog Computing, PCM-Integrated Semiconductor Circuits, Non-Volatile Analog Computing, Energy-Efficient Neuromorphic Systems, Matrix-Vector Multiplication, Edge AI.

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1. Antolini A, Lico A, Zavalloni F, Scarselli EF, Gnudi A, Torres ML, Canegallo R, Pasotti M. A readout scheme for PCM-based analog in-memory computing with drift compensation through reference conductance tracking. IEEE Open Journal of the Solid-State Circuits Society. 2024 Jul 25;4:69-82.
2. Pistolesi L, Ravelli L, Glukhov A, de Gracia Herranz A, Lopez-Vallejo M, Carissimi M, Pasotti M, Rolandi PL, Redaelli A, Martín IM, Bianchi S. Differential phase change memory (PCM) cell for drift- compensated in-memory computing. IEEE Transactions on Electron Devices. 2024 Oct 25;71(12):7447-53.
3. Yang X, Lei Y, Yu Q, Wang Q, Chen H, Song Z. Phase change memory programming circuit with improved speed. Moore and More. 2025 Jan 15;2(1):3.
4. Syed GS, Le Gallo M, Sebastian A. Phase-change memory for in-memory computing. Chemical reviews. 2025 May 22;125(11):5163-94.
5. Zhou W, Shen X, Yang X, Wang J, Zhang W. Fabrication and integration of photonic devices for phase- change memory and neuromorphic computing. International Journal of Extreme Manufacturing. 2024 Apr 1;6(2):022001..
6. Viollet V, Prenat G, Al Muktash A, Popon C, Anghel L, Lecoq X. Design of a Low-Power High- Density Analog in-Memory Computing Accelerator Using 18 Nm FD-SOI Phase-Change Memory. In2026 IEEE 27th Latin American Test Symposium (LATS) 2026 Mar 17 (pp. 1-6). IEEE.
7. Le Gallo M, Khaddam-Aljameh R, Stanisavljevic M, Vasilopoulos A, Kersting B, Dazzi M, Karunaratne G, Brändli M, Singh A, Mueller SM, Büchel J. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nature Electronics. 2023 Sep;6(9):680-93.
8. Wu C, Deng H, Huang YS, Yu H, Takeuchi I, Ríos Ocampo CA, Li M. Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films. Science Advances. 2024 Jan 5;10(1):eadk1361.
9. Miller F, Chen R, Fröch JE, Rarick H, Geiger S, Majumdar A. Rewritable photonic integrated circuits using dielectric-assisted phase-change material waveguides. Optics Letters. 2023 Apr 26;48(9):2385-8.
10. Wu C, Yu H, Lee S, Peng R, Takeuchi I, Li M. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nature communications. 2021 Jan 4;12(1):96.
11. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
12. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
13. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
14. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
15. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
16. Ranjan S, Jaiswal S, Latif A, Das DC, Sinha N, Hussain SS, Ustun TS. Isolated and interconnected multi-area hybrid power systems: A review on control strategies. Energies. 2021 Dec 8;14(24):8276.
17. Pellis S. Golden Fractals in Fluid Dynamics and Turbulence. Available at SSRN 5543579. 2025 Sep 28.
18. Golzan M, Ngatched TM, Popuri K, Zhang L. Analog and Mixed-Signal IC Modeling and Optimization: An Artificial Intelligence Perspective. ACM Transactions on Design Automation of Electronic Systems. 2025 Sep 20;30(5):1-32.
19. Dash PP, Kazerani M. Harmonic elimination in a multilevel current-source inverter-based grid- connected photovoltaic system. InIECON 2012-38th Annual Conference on IEEE Industrial Electronics Society 2012 Oct 25 (pp. 1001-1006). IEEE.

The continuous demand for high-speed, energy-efficient computing has intensified the exploration of in- memory analog computing (IMAC) architectures, which aim to minimize the latency and power overhead associated with conventional von Neumann architectures. This research investigates the integration of phase- change materials (PCMs) with semiconductor circuits to realize programmable, non-volatile in-memory analog computing platforms. PCMs exhibit reversible resistance switching between amorphous and crystalline states, enabling precise analog weight storage directly within computational elements. By embedding PCMs in CMOS- compatible circuits, the proposed architecture facilitates parallelized matrix-vector multiplication, critical for accelerating machine learning and signal processing tasks. Comprehensive device-level simulations demonstrate that PCM-integrated circuits can achieve low write energy (~pJ per operation) and high endurance (>10^9 cycles) while maintaining computational accuracy within 2% of ideal analog behavior. Additionally, circuit- level analyses reveal enhanced scalability and reduced area footprint compared to conventional SRAM-based analog computing units. In order to increase long-term dependability and computational efficiency, the study also looks at the thermal stability, switching dynamics, and data retention properties of PCM devices under various operating situations. The study provides a framework for optimizing PCM device parameters, circuit layouts, and analog computation strategies to maximize speed, energy efficiency, and reliability. These findings underscore the potential of PCM-enabled in-memory analog computing as a viable pathway for next-generation edge AI and neuromorphic systems.

Keywords - Phase-Change Material, In-Memory Analog Computing, PCM-Integrated Semiconductor Circuits, Non-Volatile Analog Computing, Energy-Efficient Neuromorphic Systems, Matrix-Vector Multiplication, Edge AI.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Antolini A, Lico A, Zavalloni F, Scarselli EF, Gnudi A, Torres ML, Canegallo R, Pasotti M. A readout scheme for PCM-based analog in-memory computing with drift compensation through reference conductance tracking. IEEE Open Journal of the Solid-State Circuits Society. 2024 Jul 25;4:69-82.
2. Pistolesi L, Ravelli L, Glukhov A, de Gracia Herranz A, Lopez-Vallejo M, Carissimi M, Pasotti M, Rolandi PL, Redaelli A, Martín IM, Bianchi S. Differential phase change memory (PCM) cell for drift- compensated in-memory computing. IEEE Transactions on Electron Devices. 2024 Oct 25;71(12):7447-53.
3. Yang X, Lei Y, Yu Q, Wang Q, Chen H, Song Z. Phase change memory programming circuit with improved speed. Moore and More. 2025 Jan 15;2(1):3.
4. Syed GS, Le Gallo M, Sebastian A. Phase-change memory for in-memory computing. Chemical reviews. 2025 May 22;125(11):5163-94.
5. Zhou W, Shen X, Yang X, Wang J, Zhang W. Fabrication and integration of photonic devices for phase- change memory and neuromorphic computing. International Journal of Extreme Manufacturing. 2024 Apr 1;6(2):022001..
6. Viollet V, Prenat G, Al Muktash A, Popon C, Anghel L, Lecoq X. Design of a Low-Power High- Density Analog in-Memory Computing Accelerator Using 18 Nm FD-SOI Phase-Change Memory. In2026 IEEE 27th Latin American Test Symposium (LATS) 2026 Mar 17 (pp. 1-6). IEEE.
7. Le Gallo M, Khaddam-Aljameh R, Stanisavljevic M, Vasilopoulos A, Kersting B, Dazzi M, Karunaratne G, Brändli M, Singh A, Mueller SM, Büchel J. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nature Electronics. 2023 Sep;6(9):680-93.
8. Wu C, Deng H, Huang YS, Yu H, Takeuchi I, Ríos Ocampo CA, Li M. Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films. Science Advances. 2024 Jan 5;10(1):eadk1361.
9. Miller F, Chen R, Fröch JE, Rarick H, Geiger S, Majumdar A. Rewritable photonic integrated circuits using dielectric-assisted phase-change material waveguides. Optics Letters. 2023 Apr 26;48(9):2385-8.
10. Wu C, Yu H, Lee S, Peng R, Takeuchi I, Li M. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nature communications. 2021 Jan 4;12(1):96.
11. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
12. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
13. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
14. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
15. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
16. Ranjan S, Jaiswal S, Latif A, Das DC, Sinha N, Hussain SS, Ustun TS. Isolated and interconnected multi-area hybrid power systems: A review on control strategies. Energies. 2021 Dec 8;14(24):8276.
17. Pellis S. Golden Fractals in Fluid Dynamics and Turbulence. Available at SSRN 5543579. 2025 Sep 28.
18. Golzan M, Ngatched TM, Popuri K, Zhang L. Analog and Mixed-Signal IC Modeling and Optimization: An Artificial Intelligence Perspective. ACM Transactions on Design Automation of Electronic Systems. 2025 Sep 20;30(5):1-32.
19. Dash PP, Kazerani M. Harmonic elimination in a multilevel current-source inverter-based grid- connected photovoltaic system. InIECON 2012-38th Annual Conference on IEEE Industrial Electronics Society 2012 Oct 25 (pp. 1001-1006). IEEE.