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1. Memimpedance‐Based Biomolecular Device for Adaptive Physical Reservoir Computing

   https://doi.org/10.1002/aisy.202500281  

   Mohamed, Ahmed S; Armendarez, Nicholas X; Hasan, Md Sakib; Najem, Joseph S (November 2025, Advanced Intelligent Systems)

   Artificial neural networks and recurrent neural networks are foundational to machine learning; although successful, their simplified components lack the intricate dynamics and high functional density of biological neural networks, limiting their efficiency. Physical reservoir computing (PRC) offers an alternative by leveraging the intrinsic dynamics of physical systems while restricting training to the readout layer. However, two significant shortcomings hinder the PRC's efficacy: the memory‐nonlinearity trade‐off, where increasing nonlinearity reduces memory capacity, and limited timescale adaptability, constraining it to contrived task‐device matching. This work presents a biomolecular PRC system that integrates heterogeneous nodes with complementary memimpedance properties using droplet interface bilayers. The system combines ion channel‐based memristive (nonlinear, fast‐switching) and biomembrane‐based memcapacitive (linear, slow‐switching) nodes within a single device, addressing the trade‐off and enabling multiscale temporal processing. Predicting a second‐order nonlinear system is demonstrated to address the trade‐off, achieving a normalized mean square error of 0.176 with the hybrid system outperforming memristive and memcapacitive reservoirs. Additionally, two real‐time classification tasks validate the system's ability to process signals across temporal scales, achieving 100% accuracy in both tasks. This approach expands PRC's applicability across diverse tasks without preoptimization, underscoring the potential of PRC and soft biomolecular materials for adaptive energy‐efficient computing. 

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2. Focused surface acoustic wave induced nano-oscillator based reservoir computing

   https://doi.org/10.1063/5.0110769  

   Chowdhury, Md. Fahim; Misba, Walid Al; Rajib, Md Mahadi; Edwards, Alexander J.; Bhattacharya, Dhritiman; Varghese, Mathew S.; Friedman, Joseph S.; Atulasimha, Jayasimha (September 2022, Applied Physics Letters)

   We demonstrate using micromagnetic simulations that a nanomagnet array excited by surface acoustic waves (SAWs) can work as a reservoir. An input nanomagnet is excited with focused SAW and coupled to several nanomagnets, seven of which serve as output nanomagnets. To evaluate memory effect and computing capability, we study the short-term memory (STM) and parity check (PC) capacities, respectively. The SAW (4 GHz carrier frequency) amplitude is modulated to provide a sequence of sine and square waves of 100 MHz frequency. The responses of the selected output nanomagnets are processed by reading the envelope of their magnetization states, which is used to train the output weights using the regression method. For classification, a random sequence of 100 square and sine wave samples is used, of which 80% are used for training, and the rest are used for testing. We achieve 100% training and 100% testing accuracy. The average STM and PC are calculated to be ∼4.69 and ∼5.39 bits, respectively, which is indicative of the proposed acoustically driven nanomagnet oscillator array being well suited for physical reservoir computing applications. The energy dissipation is ∼2.5 times lower than a CMOS-based echo-state network. Furthermore, the reservoir is able to accurately predict Mackey-Glass time series up to several time steps ahead. Finally, the ability to use high frequency SAW makes the nanomagnet reservoir scalable to small dimensions, and the ability to modulate the envelope at a lower frequency (100 MHz) adds flexibility to encode different signals beyond the sine/square waves classification and Mackey-Glass predication tasks demonstrated here. 

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3. Model-free optimization and parallel architecture towards monolithic-hybrid-photonic-electronic reservoir computing

   https://doi.org/10.1364/OE.585815  

   Gaur, Prabhav; Gao, Chengkuan; Johnson, Karl; Rubin, Shimon; Fainman, Yeshaiahu; Hsueh, Tzu-Chien (December 2025, Optics Express)

   Physical reservoir computing (PRC) is a recently developed variant of neuromorphic computing, where the output from a nonlinear physical system is utilized to perform various machine learning tasks. In this work, we theoretically analyze the performance of a photonic waveguide mesh (WGM) with electro-optic phase shifters for monolithic-hybrid-photonic-electronic reservoir computing (MHPE RC), where the phase-to-intensity relations in the photonic circuit provide nonlinearity and high dimensionality, while the electronic circuit provides the input and feedback with tunable parameters. First, we numerically demonstrate the efficiency and performance superiority of a parallel architecture comprising fabricated WGM. Next, we present the Lyapunov filtered-minimal redundancy maximal relevance (Lf-mRMR) algorithm, which optimizes the electronic parameters of parallel WGMs by analyzing the Lyapunov exponent and the mutual information between the output of the corresponding WGMs and the required task. The Lf-mRMR algorithm is computationally less complex, substantially improves the performance of MHPE RC, and can tolerate fabrication errors. We present the selective parallel architecture for reservoir computing (SPARC), which, assisted by the Lf-mRMR algorithm, can achieve performance close to convolutional neural networks. Finally, we experimentally employ on-chip silicon photonics with thermo-optical phase shifters and external off-chip digital memory and control unit to validate the advantageous performance of Lf-mRMR-assisted RC. 

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4. Physical reservoir computing with origami: a feasibility study

   Bhovad, Priyanka; Li, Suyi (March 2021, Proc. SPIE 11589, Behavior and Mechanics of Multifunctional Materials XV)

   null (Ed.)

   In the field of soft robotics, harnessing the nonlinear dynamics of soft and compliant bodies as a computational resource to enable embodied intelligence and control is known as morphological computation. Physical reservoir computing (PRC) is a true instance of morphological computation wherein; a physical nonlinear dynamic system is used as a fixed reservoir to perform complex computational tasks. These dynamic reservoirs can be used to approximate nonlinear dynamical systems and even perform machine learning tasks. By numerical simulation, this study illustrates that an origami meta-material can also be used as a dynamic reservoir for pattern generation, output modulation, and input sensing. These results could pave the way for intelligently designed origami-based robots that interact with the environment through a distributed network of sensors and actuators. This embodied intelligence will enable the next generations of soft robots to autonomously coordinate and modulate their activities, such as locomotion gait generation and limb manipulation while resisting external disturbances. 

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5. A Dual‐Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High‐Speed Reservoir Computing

   https://doi.org/10.1002/aelm.202500844  

   Wang, Yifan; Shahriar, Muhammad Sakib; Soliman, Salma; Vaillancourt, Noah; Fernandes, Lance; Padovani, Andrea; Khan, Asif Islam; Hasan, Md Sakib; Islam, Raisul (July 2026, Advanced Electronic Materials)

   ABSTRACT The exponential growth of edge artificial intelligence demands material‐focused solutions to overcome energy consumption and latency limitations when processing real‐time temporal data. Physical reservoir computing (PRC) offers an energy‐efficient paradigm but faces challenges due to limited device scalability and reconfigurability. Additionally, reservoir and readout layers require memory of different timescales, short‐term and long‐term respectively‐a material challenge hindering CMOS‐compatible implementations. This work demonstrates a CMOS‐compatible ferroelectric transistor using hafnium‐zirconium‐oxide (HZO) and silicon, enabling dual‐memory operation. This system exhibits non‐volatile long‐term memory (LTM) from ferroelectric HZO polarization and volatile short‐term memory (STM) from engineered non‐quasi‐static (NQS) channel‐charge relaxation driven by gate‐source/drain overlap capacitance. Ferroelectric polarization acts as non‐volatile programming of volatile dynamics: by modulating threshold voltage, the ferroelectric state deterministically switches the NQS time constant and computational behavior between paired‐pulse facilitation (PPF) and depression (PPD). This establishes a generalizable material‐design principle applicable to diverse ferroelectric‐semiconductor heterostructures, extending beyond silicon to oxide semiconductors and heterogeneously‐integrated systems. The device solves second‐order nonlinear tasks with 3.69 10 normalized error using only 16 reservoir states‐ reduction—achieving 20 response time ( faster) and 1.5 10 J energy consumption, providing an immediately manufacturable pathway for neuromorphic hardware and energy‐efficient edge intelligence. 

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